October 25, 2021

Global Economy and Development

Strengthening international cooperation on AI

BY Brookings Institute, Report

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Strengthening international cooperation on AI 1

Executive Summary

International cooperation on artificial intelligence—why, what, and how

Since 2017, when Canada became the first country to adopt a national AI strategy, at least 60 countries have adopted some form of policy for artificial intelligence (AI). The prospect of an estimated boost of 16 percent, or US$13 trillion, to global output by 2030 has led to an unprecedented race to promote AI uptake across industry, consumer markets, and government services. Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.

At the same time, the work on developing global standards for AI has led to significant developments in various international bodies. These encompass both technical aspects of AI (in standards development organizations (SDOs) such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE) among others) and the ethical and policy dimensions of responsible AI. In addition, in 2018 the G-7 agreed to establish the Global Partnership on AI, a multistakeholder initiative working on projects to explore regulatory issues and opportunities for AI development. The Organization for Economic Cooperation and Development (OECD) launched the AI Policy Observatory to support and inform AI policy development. Several other international organizations have become active in developing proposed frameworks for responsible AI development.

In addition, there has been a proliferation of declarations and frameworks from public and private organizations aimed at guiding the development of responsible AI. While many of these focus on general principles, the past two years have seen efforts to put principles into operation through fully-fledged policy frameworks. Canada’s directive on the use of AI in government, Singapore’s Model AI Governance Framework, Japan’s Social Principles of Human-Centric AI, and the U.K. guidance on understanding AI ethics and safety have been frontrunners in this sense; they were followed by the U.S. guidance to federal agencies on regulation of AI and an executive order on how these agencies should use AI. Most recently, the EU proposal for adoption of regulation on AI has marked the first attempt to introduce a comprehensive legislative scheme governing AI.

Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.

In exploring how to align these various policymaking efforts, we focus on the most compelling reasons for stepping up international cooperation (the “why”); the issues and policy domains that appear most ready for enhanced collaboration (the “what”); and the instruments and forums that could be leveraged to achieve meaningful results in advancing international AI standards, regulatory cooperation, and joint R&D projects to tackle global challenges (the “how”). At the end of this report, we list the topics that we propose to explore in our forthcoming group discussions.

Why international cooperation on AI is important

Even more than many domains of science and engineering in the 21st century, the international AI landscape is deeply collaborative, especially when it comes to research, innovation, and standardization. There are several reasons to sustain and enhance international cooperation.

  1. AI research and development is an increasingly complex and resource-intensive endeavor, in which scale is an important advantage. Cooperation among governments and AI researchers and developers across national boundaries can maximize the advantage of scale and exploit comparative advantages for mutual benefit. An absence of international cooperation would lead to competitive and duplicative investments in AI capacity, creating unnecessary costs and leaving each government worse off in AI outcomes. Several essential inputs used in the development of AI, including access to high-quality data (especially for supervised machine learning) and large-scale computing capacity, knowledge, and talent, benefit from scale.
  2. International cooperation based on commonly agreed democratic principles for responsible AI can help focus on responsible AI development and build trust. While much progress has been made aligning on responsible AI, there remain differences—even among Forum for Cooperation on AI (FCAI) participants. The next steps in AI governance involve translating AI principles into policy, regulatory frameworks, and standards. These will require deeper understanding of how AI works in practice and working through the operation of principles in specific contexts and in the face of inevitable tradeoffs, such as may arise when seeking AI that is both accurate and explainable. Effective cooperation will require concrete steps in specific areas, which the recommendations of this report aim to suggest.
  3. When it comes to regulation, divergent approaches can create barriers to innovation and diffusion. Governments’ efforts to boost domestic AI development around concepts of digital sovereignty can have negative spillovers, such as restrictions on access to data, data localization, discriminatory investment, and other requirements. Likewise, diverging risk classification regimes and regulatory requirements can increase costs for businesses seeking to serve the global AI market. Varying governmental AI regulations may necessitate building variations of AI models that can increase the work necessary to build an AI system, leading to higher compliance costs that disproportionately affect smaller firms. Differing regulations may also force variation in how data sets are collected and stored, creating additional complexity in data systems and reducing the general downstream usefulness of the data for AI. Such additional costs may apply to AI as a service as well as hardware-software systems that embed AI solutions, such as autonomous vehicles, robots, or digital medical devices. Enhanced cooperation is key to create a larger market in which different countries can try to leverage their own competitive advantage. For example, the EU seeks to achieve a competitive advantage in “industrial AI”: EU enterprises could exploit that AI without the prospect of having to engage in substantial reengineering to meet requirements of another jurisdiction.
  4. Aligning key aspects of AI regulation can enable specialized firms in AI development to thrive. Such companies generate business by developing expertise in a specialized AI system, then licensing these to other companies as one part of a broader tool. As AI becomes more ubiquitous, complex stacks of specialized AI systems may emerge in many sectors. A more open global market would allow a company to take advantage of digital supply chains, using a single product with a natural language model built in Canada, a video analysis algorithm trained in Japan, and network analysis developed in France. Enabling global competition by such specialized firms will encourage healthier markets and more AI innovation.
  5. Enhanced cooperation in trade is essential to avoid unjustified restrictions to the flow of goods and data, which would substantially reduce the prospective benefits of AI diffusion. While the strategic importance of data and sovereignty has in many countries given rise to legitimate industrial policy initiatives aimed at mapping and reducing dependencies on the rest of the world, protectionist measures can jeopardize global cooperation, impinge on global value chains, and negatively affect consumer choice, thereby reducing market size and overall incentives to invest in meaningful AI solutions.
  6. Enhanced cooperation is needed to tap the potential of AI solutions to address global challenges. No country can “go it alone” in AI, especially when it comes to sharing data and applying AI to tackle global challenges like climate change or pandemic preparedness. The governments involved in the FCAI share interests in deploying AI for global social, humanitarian, and environmental benefit. For example, the EU is proposing to employ AI to support its Green Deal, and the G-7 and GPAI have called for harnessing AI for U.N. Sustainable Development Goals. Collaborative “moonshots” can pool resources to leverage the potential of AI and related technologies to address key global problems in domains such as health care, climate science, or agriculture at the same time as they provide a way to test approaches to responsible AI together.
  7. Cooperation among likeminded countries is important to reaffirm key principles of openness and protection of democracy, freedom of expression, and other human rights. The risks associated with the unconstrained use of AI solutions by techno-authoritarian regimes— such as China’s—expose citizens to potential violations of human rights and threaten to split cyberspace into incompatible technology stacks and fragment the global AI R&D process.

The fact that international cooperation is an element of most governments’ AI strategies indicates that governments appreciate the connection between AI development and collaboration across borders. This report is about concrete ways to realize this connection.

At the same time, international cooperation should not be interpreted as complete global harmonization: countries legitimately differ in national strategic priorities, legal traditions, economic structures, demography, and geography. International collaboration can nonetheless create the level playing field that would enable countries to engage in fruitful “co-opetition” in AI: agreeing on basic principles and when possible seeking joint outcomes, but also competing for the best solutions to be scaled up at the global level. Robust cooperation based on common principles and values is a foundation for successful national development of AI.

Rules, standards, and R&D projects: Key areas for collaboration

Our exploration of international AI governance through roundtables, other discussions, and research led us to identify three main areas where enhanced collaboration would provide fruitful: regulatory policies, standard-setting, and joint research and development (R&D) projects. Below, we summarize ways in which cooperation may unfold in each of these areas, as well as the extent of collaboration conceivable in the short term as well as in the longer term.

Cooperation on regulatory policy

AI policy development is in the relatively early stages in all countries, and so timely and focused international cooperation can help align AI policies and regulations.

International regulatory cooperation has the potential to reduce regulatory burdens and barriers to trade, incentivize AI development and use, and increase market competition at the global level. That said, countries differ in legal tradition, economic structure, comparative advantage in AI, weighing of civil and fundamental rights, and balance between ex ante regulation and ex post enforcement and litigation systems. Such differences will make it difficult to achieve complete regulatory convergence. Indeed, national AI strategies and policies reflect differences in countries’ willingness to move towards a comprehensive regulatory framework for AI. Despite these differences, AI policy development is in the relatively early stages in all countries, and so timely and focused international cooperation can help align AI policies and regulations.

Against this backdrop, it is reasonable to assume that AI policy development is less embedded in pre-existing legal tradition or frameworks at this stage, and thus that international cooperation in this field can achieve higher levels of integration. The following areas for cooperation emerged from the FCAI dialogues and our other explorations.

  • Building international cooperation into AI policies. FCAI governments should give effect to their recognition of the need for international engagement on AI by committing to pursue coordination with each other and other international partners prior to adopting domestic AI initiatives.
  • A common, technology-neutral definition of AI for regulatory purposes. Based on the definitions among FCAI participants and the work of the OECD expert group, converging on a common definition of AI and working together to gradually update the description of an AI system, and its possible configurations and techniques, appears feasible and already partly underway. A common definition is important to guide future cooperation in AI and determines the level of ambition that can be reached by such a process.
  • Building on a risk-based approach to AI regulation. A variety of governments and other bodies have endorsed a risk-based approach to AI in national strategies and in bilateral or multilateral contexts. Most notably, a risk-based approach is central to the policy frameworks of the two most prominent exemplars of AI policy development—the U.S. and the EU. These recent, broadly parallel developments have opened the door to developing international cooperation on ways to address risks while maximizing benefits. However, there remain challenges to convergence on a risk-based approach. Dialogue on clear identification and classification of risks, approaches to benefit-risk analysis, possible convergence on cases in which the risks are too high to be mitigated, and the type of risk assessment to be performed and who should perform it, would greatly benefit cooperation on a risk-based approach.
  • Sharing experiences and developing common criteria and standards for auditing AI systems. The field of accountability in AI and algorithms has been the subject of wide and valuable work by civil society organizations as well as governments. The exchange of good practices and—ultimately—a common, or at least a compatible, framework for AI auditing would eliminate significant barriers to the development of a truly international market for AI solutions. It also would facilitate the emergence of third-party auditing standards and an international market for AI auditing, with potential benefits in terms of quality, price, and access for auditing services for deployers of AI. Additionally, exchange of practices and international standards for AI auditing, monitoring, and oversight would significantly help the policy community keep up to speed in market monitoring.
  • A joint platform for regulatory sandboxes. Even without convergence on risk assessments or regulatory measures, an international platform for regulatory learning involving all governments that participate in FCAI and possibly others is a promising avenue for deepening international cooperation on AI. Such a platform could host an international repository of ongoing experiments on AI-enabled innovations, including regulatory sandboxes. As use of sandboxes becomes a more common way for governments to test the viability and conformity of new AI solutions under legislative and regulatory requirements, updating information on ongoing government initiatives could save resources and inform AI developers and policymakers. Aligning the criteria and overall design of AI sandboxes in different administrations could also increase the prospective benefits and impact of these processes, as developers willing to enter the global market might be able to go through the sandbox process in a single participating country.
  • Cooperation on AI use in government: procurement and accountability. A natural candidate for further exchange and cooperation in FCAI is the adoption of AI solutions in government, including both “back office” solutions and more public-facing applications. The sharing of good practices and overall lessons on what works when deploying AI in government would also be an important achievement. Important areas in this respect are procurement and effective oversight of deployment.
  • Sectoral cooperation on AI use cases. A sector-specific approach can ensure higher levels of regulatory certainty. In sectors like finance, key criteria such as fairness, discrimination, and transparency have long been subject to extensive regulatory intervention, and sectoral regulation must ensure continuity while accounting for the increasing use of AI. In health and pharmaceuticals, the use of AI both as a stand-alone solution and embedded in medical devices has prompted a very specific, technical discussion regarding the risk-based approach to be adopted and has already enabled valuable sectoral initiatives. The adoption of different standards and criteria in sectoral regulation may increase regulatory costs for developers willing to serve more than one sector and country with their AI solutions. In such a cross-cutting framework, examples from mature areas of regulation such as finance and health can also become a form of regulatory sandbox to model regulation for other sectors in the future.

Cooperation on sharing data across borders

Data governance is a focal area for international cooperation on AI because of the importance of data as an input for AI R&D and because of the added complexity of regulatory regimes already in place that restrict certain information flows, including data protection and intellectual property laws. Effective international cooperation on AI needs a robust and coherent framework for data protection and data sharing. There are a variety of channels addressing these issues including the Asia-Pacific Economic Cooperation group, the working group on data governance of the Global Partnership on AI, and bilateral discussions between the EU and U.S. Nonetheless, the potential impact of such laws on data available for AI-driven medical and scientific research requires specific focus as the EU both reviews its General Data Protection Regulation and considers new legislation on private and public sector data sharing.

There are other significant data governance issues that may benefit from pooled efforts across borders that, by and large, are the subject of international cooperation. Key areas in this respect include opening government data including international data sharing, improving data interoperability, and promoting technologies for trustworthy data sharing.

Cooperation on international standards for AI

As countries move from developing frameworks and policies to more concrete efforts to regulate AI, demand for AI standards will grow. These include standards for risk management, data governance, and technical documentation that can establish compliance with emerging legal requirements. International AI standards will also be needed to develop commonly accepted labeling practices that can facilitate business-to-business (B2B) contracting and to demonstrate conformity with AI regulations; address the ethics of AI systems (transparency, neutrality/lack of bias, etc.); and maximize the harmonization and interoperability for AI systems globally. International standards from standards development organizations like the ISO/IEC and IEEE can help ensure that global AI systems are ethically sound, robust, and trustworthy, that opportunities from AI are widely distributed, and that standards are technically sound and research-driven regardless of sector or application.

International standards from standards development organizations like the ISO/IEC and IEEE can help ensure that global AI systems are ethically sound, robust, and trustworthy, that opportunities from AI are widely distributed, and that standards are technically sound and research-driven regardless of sector or application.

The governments participating in the FCAI recognize and support industry-led standards setting. While there are differences in how the FCAI participants engage with industry-led standards bodies, a common element is support for the central role of the private sector in driving standards. That said, there is a range of steps that FCAI participants can take to strengthen international cooperation in AI standards. The approach of FCAI participants that emphasizes an industry-led approach to developing international AI standards contrasts with the overall approach of other countries, such as China, where the state is at the center of standards making activities. The more direct involvement by the Chinese government in setting standards, driving the standards agenda, and aligning these with broader Chinese government priorities requires attention by all FCAI participants with the aim of encouraging Chinese engagement in international AI standard-setting consistent with outcomes that are technically robust and industry driven.

Sound AI standards can also support international trade and investment in AI, expanding AI opportunity globally and increasing returns to investment in AI R&D. The World Trade Organization (WTO) Technical Barriers to Trade (TBT) Agreement’s relevance to AI standards is limited by its application only to goods, whereas many AI standards will apply to services. Recent trade agreements have started to address AI issues, including support for AI standards, but more is needed. An effective international AI standards development process is also needed to avoid bifurcated AI standards—centered around China on the one hand and the West on the other. Which outcome prevails will to some extent depend on progress in effective international AI standards development.

R&D cooperation: Selecting international AI projects

Productive discussion of AI ethics, regulation, risks, and benefits requires use cases because the issues are highly contextual. As a result, AI policy development has tended to move from broad principles to specific sectors or use cases. Considering this need, we suggest that developing international cooperation on AI would benefit from putting cooperation into operation with specific use cases. To this end, we propose that FCAI participants expand efforts to deploy AI on important global problems collectively by working toward agreement on joint research aimed at a specific development project (or projects). Such an effort could stimulate development of AI for social benefit and also provide a forcing function for overcoming differences in approaches to AI policy and regulation.

Criteria for the kinds of goals or projects to consider include the following:

  1. Global significance. The project should be aimed at important global issues that demand transnational solutions. The shared importance of the issues should give all participants a common stake and, if successful, could contribute toward global welfare.
  2. Global scale. The problem and the scope of the project should require resources on a large enough scale that the pooled support of leading governments and institutions adds significant value.
  3. A public good. Given its significance and scale, the project would amount to a public good. In turn, the output of the project should also be a public good and both the project and the output should be available to all participants and less developed countries.
  4. A collaborative test bed. Governance of the project is likely to necessitate addressing regulatory, ethical, and risk questions in a context that is concrete and in which the participants have incentives to achieve results. It would amount to a very large and shared regulatory sandbox.
  5. Assessable impact. The project will need to be monitored commensurately with its scale, public visibility, and experimental nature. Participants will need to assess progress toward both defined project goals and broader impact.
  6. A multistakeholder effort. Considering its public importance and the resources it should marshal, the project will need to be government-initiated. But the architecture and governance should be open to nongovernmental participation on a shared basis.

This proposal could be modeled on several large-scale international scientific collaborations: CERN, the Human Genome Project, or the International Space Station. It would also build on numerous initiatives toward collaborative research and development on AI. Similar global collaboration will be more difficult in a world of increased geopolitical and economic competition, nationalism, nativism, and protectionism among governments that have been key players in these efforts.


Below, we present recommendations for developing international cooperation on AI based on our discussions and work to date.

R1. Commit to considering international cooperation in drafting and implementing national AI policies.

This recommendation could be implemented within a relatively short timeframe and initially would take the form of firm declarations by individual countries. Ultimately this could lead to a joint declaration with clear commitments on the part of the governments involved.

R2. Refine a common approach to responsible AI development.

This type of recommendation requires enhanced cooperation between FCAI governments, which can then provide a good basis for incremental forms of cooperation.

R3. Agree on a common, technology-neutral definition of AI systems.

FCAI governments should work on a common definition of AI that is technology-neutral and broad. This recommendation can be implemented in a relatively short term and requires joint action by FCAI governments. The time to act is short, as the rather broad definition given in the EU AI Act is still undergoing the legislative process in the EU and many other countries are still shaping their AI policy frameworks.

R4. Agree on the contours of a risk-based approach.

Alignment on this key element of AI policy would be an important step towards an interoperable system of responsible AI. It would also facilitate cooperation among FCAI governments, industry, and civil society working on AI standards in international SDOs. General agreement on a risk-based approach could be achieved in the short term; developing the contours of a risk-based classification system would probably take more time and require deeper cooperation among FCAI governments as well as stakeholders.

R5. Establish “redlines” in developing and deploying AI.

This may entail an iterative process. FCAI governments could agree on an initial, limited list of redlines such as certain AI uses for generalized social scoring by governments; and then gradually expand the list over time to include emerging AI uses on which there is substantial agreement on the need to prohibit use.

R6. Strengthen sectoral cooperation, starting with more developed policy domains.

Sectoral cooperation can be organized on relatively short timeframes starting from sectors that have well-developed regulatory systems and present higher risks, such as health care, transport and finance, in which sectoral regulation already exists, and its adaptation to AI could be achieved relatively swiftly.

R7. Create a joint platform for regulatory learning and experiments.

A joint repository could stimulate dialogue on how to design and implement sandboxes and secure sound governance, transparency, and reproducibility of results, and aid their transferability across jurisdictions and categories of users. This recommended action is independent of others and is feasible in the short term. It requires soft cooperation, in the form of a structured exchange of good practices. Over time, the repository should become richer in terms of content, and therefore more useful.

R8. Step up cooperation and exchange of practices on the use of AI in government.

FCAI governments could set up, either as a stand-alone initiative or in the context of a broader framework for cooperation, a structured exchange on government uses of AI. The dialogue may involve AI applications to improve the functioning of public administration such as the administration of public benefits or health care; AI-enabled regulation and regulatory governance practices; or other decision-making and standards and procedures for AI procurement. This recommended action could be implemented in the short term, although collecting all experiences and setting the stage for further cooperation would require more time.

R9. Step up cooperation on accountability.

FCAI governments could profit from enhanced cooperation on accountability, whether through market oversight and enforcement, auditing requirements, or otherwise. This could combine with sectoral cooperation and possibly also with standards development for auditing AI systems.

R10. Assess the impact of AI on international data governance.

There is a need for a common understanding of how data governance rules affect AI R&D in areas such as health research and other scientific research, and whether they inhibit the exploration that is an essential part of both scientific discovery and machine learning. There is also need for a critical look at R&D methods to develop a deeper understanding of appropriate boundaries on use of personal data or other protected information. In turn, there is also a need to expand R&D and understanding in privacy-protecting technologies that can enable exploration and discovery while protecting personal information.

R11. Adopt a stepwise, inclusive approach to international AI standardization.

A stepwise approach to standards development is needed to allow time for technology development and experimentation and to gather the data and use cases to support robust standards. It also would ensure that discussions at the international level happen once technology has reached a certain level of maturity or where a regulatory environment is adopted. To support such an approach, it would be helpful to establish a comprehensive database of AI standards under development at national and international levels.

R12. Develop a coordinated approach to AI standards development that encourages Chinese participation consistent with an industry-led, research-driven approach.

There is currently a risk of disconnect between growing concern among governments and national security officials alarmed by Chinese engagement in the standards process on the one hand, and industry participants’ perceptions of the impact of Chinese participation in SDOs on the other. To encourage constructive involvement and discourage self-serving standards, FCAI participants (and likeminded countries) should encourage Chinese engagement in international standards setting while also agreeing on costs for actions that use SDOs strategically to slow down or stall standards making. This can be accomplished through trade and other measures but will require cooperation among FCAI participants to be effective.

R13. Expand trade rules for AI standards.

The rules governing use of international standards in the WTO TBT Agreement and free trade agreements are limited to goods only, whereas AI standards will apply mainly to services. New trade rules are needed that extend rules on international standards to services. As a starting point, such rules should be developed in the context of bilateral free trade agreements or plurilateral agreements, with the aim to make them multilateral in the WTO. Trade rules are also needed to support data free flow with trust and to reduce barriers and costs to AI infrastructure. Consideration also should be given to linking participation in the development of AI standards in bodies such as ISO/IEC, with broader trade policy goals and compliance with core WTO commitments.

R14. Increase funding for participation in SDOs.

Funding should be earmarked for academics and industry participation in SDOs, as well as for SDO meetings in FCAI countries and more broadly in less developed countries. Broadened participation is important to democratize the standards making process and strengthen the legitimacy and adoption of the resulting standards. Hosting meetings of standards bodies in diverse countries can broaden exposure to standards-setting processes around AI and critical technology.

R15. Develop common criteria and governance arrangements for international large-scale R&D projects.

Joint research and development applying to large-scale global problems such as climate change or disease prevention and treatment can have two valuable effects: It can bring additional resources to the solution of pressing global challenges, and the collaboration can help to find common ground in addressing differences in approaches to AI. FCAI will seek to incubate a concrete roadmap on such R&D for adoption by FCAI participants as well as other governments and international organizations. Using collaboration on R&D as a mechanism to work through matters that affect international cooperation on AI policy means that this recommendation should play out in the near term.

Proposed future topics for FCAI dialogues

– Scaling R&D cooperation on AI projects.
– China and AI: what are the risks, opportunities, and ways forward?
– Government use of AI: developing common approaches.
– Regulatory cooperation and harmonization: issues and mechanisms.
– A suitable international framework for data governance.
– Standards development.
– An AI trade agreement: partners, content, and strategy.

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Biden’s nominees would bring diversity to the Fed—if they’re confirmed

President Biden has announced his roster to fill key vacancies on the Federal Reserve’s 7-seat Board of Governors. If confirmed by the Senate, Biden’s nominees would advance his economic agenda at the central bank. They would diversify the ranks of economic policymakers and likely tighten supervision of Wall Street.

Sarah A. Binder

Senior Fellow – Governance Studies



Mark Spindel

Chief Investment Officer, Potomac River Capital LLC

These nominations follow in the wake of Biden’s decisions late last year to reappoint Jerome Powell to a second term as Fed chair and to elevate Lael Brainard as second in command. Powell and Brainard already serve as confirmed governors, but the Senate will also need to approve their four-year leadership posts. If the Senate confirms all five, Biden’s Fed appointees would reverse the heavy GOP-tilt of the Board engineered by the Trump administration.  
Here’s what you need to know.
Diversity counts
Biden has nominated two Black economists, Michigan State’s Lisa Cook and Davidson College’s Phillip Jefferson, to seats on the Board. He has also named former Fed governor and Treasury official, Sarah Bloom Raskin, as the Fed’s vice chair of supervision, a position Congress created in the wake of the global financial crisis as the Fed’s top banking cop.
These appointments help to diversify the Fed’s almost exclusively white ranks. Since Congress revamped the Federal Reserve Act in 1935, creating the 7-seat Board of Governors, 82 people have served on the Board. Just three of them were Black men, and ten of them were white women. And while Biden’s nominations augment the Fed’s racial diversity, confirming Cook, Brainard, and Raskin would expand the number of women governors by just one, since both Raskin and Brainard already have Board service under their belts. Notably though, this would be the first Board with a majority (four) of seven seats filled by women governors.
Rough waters ahead?
Observers expect a broad swath of Senate Republicans to vote to confirm Powell, a Republican, to a second term as chair. However, it remains to be seen how many, if any, Republicans will vote to confirm the other four nominees. Of course, Senate Democrats—if they stick together—can confirm all four without any GOP support, since Democrats banned nomination filibusters back in 2013.
Like most Congressional decisions, Fed confirmation votes are more contentious today than they were even 15 years ago, before the global financial crisis. The figure below shows shrinking Senate support on final confirmation votes for Fed nominations since the Reagan administration. Of those nominees considered on the Senate floor between 1982 and 2011, only one, Alice Rivlin, received less than 94% of the vote. The most dramatic contests came in 2020: The GOP-led Senate rejected Trump’s nominee, Judy Shelton, by a vote of 47-50, and just barely confirmed another Trump nominee, Christopher Waller. Four other Trump picks never even made it to a floor vote.

Nor can Biden count on filling the Board swiftly. Prior to the financial crisis, nominees waited about three months on average for confirmation. After the crisis, the wait time ballooned closer to eight months. The Senate took nearly ten months to confirm Waller, a record delay for the contemporary Senate’s handling of Fed nominees. Even with Democrats in control this year, Republicans have found ways to slow down the Senate.
Beware partisan crosshairs
Decades of rising partisanship are seeping into senators’ views of the Fed, often turning otherwise low profile Board nominations into politically charged votes. At the same time, public attention to the Fed has grown with its expanding imprint on the economy.   
The central bank has played an outsized role in stemming the economic damage caused by the global financial crisis in 2007-08 and the global Coronavirus pandemic in 2020-21. And with interest rates near zero, central bankers need to use more creative and often contentious tools to manage the US economy. Critics from both sides of the partisan aisle blame the Fed for either doing too much—or too little—to stem an array of old and new problems.
Add in rising expectations that the Fed will hike interest rates early this year to combat inflation and a hot economy, these nominees will face questions at the core of central banking—how fast and how soon to take away the punchbowl. Raising the price of money is never easy, but this Board could find tightening especially difficult given the addition of Biden’s governors committed to the Fed’s goal of a stronger and more racially inclusive labor market. 
The parties also disagree about whether the Fed can or should do more to combat climate change, especially in light of Congress’s own tentative steps. Democrats want the Fed to use its supervisory powers to force banks to address climate risk in their lending decisions; Republicans think such policies fall outside the Fed’s mandate. Partisans also contest whether the Fed should do more to redress racial economic inequities.
Presidents use appointments to advance their agendas. The Fed is no exception, despite the myth that central banks like the Fed are “independent.” But given the often partisan Senate confirmation process, Democrats will likely need to hang together to get Biden’s picks over the finish line.

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The populist backlash in Chapter 11

From a bankruptcy perspective, the pandemic has unfolded differently than many expected. Prior economic crises have caused sharp upswings in bankruptcy filings. The 2007-2009 crisis was true to form, with business bankruptcy filings doubling during this time, to 60,837 in 2009 from 28,322 in 2007.1 Given that governments almost completely shut down the American economy in 2020, an even greater surge seemed likely. Many observers predicted a massive wave of bankruptcies.2 Bankruptcy scholars and bankruptcy organizations sprang into action, calling for Congress to increase the capacity of the bankruptcy system (primarily by increasing the number of bankruptcy judges) and to assure access to financing for companies that filed for bankruptcy.3

David Skeel

S. Samuel Arsht Professor Corporate Law – University of Pennsylvania Law School

The big surprise of the current pandemic is that the great bankruptcy wave of 2020 never materialized. The number of very large corporate bankruptcies increased,4 but overall business bankruptcies went down rather than up (from 22,780 in 2019 to 21,655 in 2020), and the decrease in consumer bankruptcy filings was even more dramatic (752,160 in 2019, 522,808 in 2020, a 28% drop).5 The most obvious reason for the surprising decline in bankruptcy filings was the enormous amount of stimulus money that buoyed the economy, including well over $1 trillion of business lending capacity in the CARES Act of March 2020 and subsequent boosters of the small business portion of the legislation. In addition, the buoyancy of the stock market provided access to equity capital for firms that might have found themselves in bankruptcy under other circumstances.
Although the pandemic confounded the typical pattern of rising bankruptcies during an economic crisis, in another respect the pandemic has proved true to form: It has provoked a populist backlash. During the 2007-2009 crisis, populist movements emerged on both ends of the political spectrum—the Tea Party on the right and Occupy Wall Street on the left—in each case, protesting bailouts of large financial institutions.
The current crisis has prompted another populist backlash, as can be seen in controversies that have arisen in the Purdue Pharma opioid bankruptcy and in the bankruptcy of USA Gymnastics after revelation of horrendous sexual abuse by former team doctor Larry Nassar. Unlike the Tea Party and Occupy Wall Street, the current outrage is directed at the bankruptcy process itself. There is a growing populist perception that Chapter 11—the bankruptcy provisions used to restructure financially distressed businesses—has become deeply unfair.  It benefits insiders—the “haves”—at the expense of outsiders—the “have nots.”
The closest analogy to the current populist backlash comes not from the most recent pre-pandemic crisis but much earlier, during the Great Depression.6 After emerging in the second half of the nineteenth century, the American approach to corporate reorganization (originally known as “equity receivership”) came to be dominated by large Wall Street banks such as J.P. Morgan and large Wall Street law firms such as Cravath, Swaine & Moore. The banks that had underwritten a class of bonds would offer to represent the investors who bought the bonds in negotiations with a financially distressed railroad or other business. In the 1930s, New Deal reformers such as William Douglas—a Yale law professor who became chairman of the Securities & Exchange Commission and later a Supreme Court Justice—concluded that the Wall Street banks and lawyers were profiting (through the fees they charged and by assuming positions of control) at the expense of the investors they purposed to represent. The reformers ripped control from Wall Street by persuading Congress to enact, and President Roosevelt to sign, the Chandler Act of 1938. The Chandler Act prohibited bankers or lawyers that had represented a company before bankruptcy from representing it after the bankruptcy filing, which meant the company’s underwriters could no longer run the reorganization process. Within a few years, Wall Street had disappeared from bankruptcy.
The pandemic has spurred a remarkably similar populist backlash. Even before the pandemic, concerns were growing about current developments in the restructuring of large corporations. Critics complained about companies’ ability to file for bankruptcy almost anywhere they want to (“forum shopping”), insider control of the restructuring process, the payment of bonuses to managers before and during bankruptcy, and the use of bankruptcy in cases like Purdue Pharma to resolve not only the obligations of the company itself but also of individuals or entities like the Sacklers who have not filed for bankruptcy themselves. During the pandemic, discontent with current bankruptcy practice has grown considerably.7 Lawmakers have introduced a spate of bills, each of which has been prompted by populist dissatisfaction with current Chapter 11 practice.

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This report describes and comments on four practices that have prompted populist backlash. Several other controversial features of current practice that are not considered here are referenced in the footnote below.8
Bankruptcy venue
The first and most longstanding magnet for populist outcry is a company’s choice of where to file its bankruptcy case—known as bankruptcy “venue.” Under the current filing rule a company can file for bankruptcy in any of the following locations: where its headquarters are; its principal assets are; it is domiciled; or an “affiliate” of the company has already filed for bankruptcy.9 Although this sounds like a limited set of options, in practice a company can file its bankruptcy case almost anywhere in the country due to the “affiliate” option. If a Pennsylvania company wished to file for bankruptcy in South Dakota, it could simply create a new, wholly owned entity in South Dakota and have the new entity file for bankruptcy in South Dakota. The Pennsylvania company could then file for bankruptcy in South Dakota since an “affiliate” is in bankruptcy there.
During the decade after the current bankruptcy code was enacted in 1970s, many large corporate debtors filed for bankruptcy in the Southern District of New York. Starting in 1990, Delaware joined New York as another popular filing location for large corporate debtors. The late 1990s saw the first serious challenge to this “forum shopping.” Critics complained that New York and Delaware judges lured companies to their districts by, among other things, allowing bankruptcy lawyers to charge high fees, quickly approving all of the debtor’s initial (“first day order”) requests, and by authorizing rapid sales of the debtors’ assets.10 They also complained that New York and Delaware were too inconvenient for employees and small creditors of companies whose operations were in other states, which it made it impossible for small parties to participate.
Venue reform was never enacted, but it continued to percolate, with support from both Democrats and Republicans. In recent years, several other locations have joined New York and Delaware as popular venues, including Richmond, Virginia and most recently the Southern District of Texas (Houston). The new twist in the controversy is that debtors in several of these locations can pick not just the district where they file but the particular judge.11 The Southern District of Texas has made this easy by committing to assign all large Chapter 11 cases to two judges in the district. In Southern District of New York, a debtor that files its bankruptcy case in White Plains was, until late last year, certain to get Judge Robert Drain, the only Southern District of New York judge sitting in White Plains.12 Purdue Pharma appears to have filed its case there for this reason.
Congress is currently considering legislation sponsored by Senators Cornyn (R-TX) and Warren (D-MA) that would ban venue shopping.13 Under the proposed legislation, large corporate debtors would generally be required to file for bankruptcy in the state where their headquarters or principal assets are.14 The reform would remove domicile—the state where a debtor is incorporated—as a venue option, and the debtor could only file for bankruptcy where an affiliate has filed if the affiliate owns a majority of the debtor’s stock—that is, if the affiliate is the parent corporation.
As often is the case with populist measures, the proposed legislation has beneficial features but also deeply problematic ones. Some of the forum shopping concerns are well taken.  Debtors should not be able to pick particular judges within a district and permitting a debtor to file anywhere an affiliate has filed is too easy to manipulate. But removing a debtor’s ability to file in its domicile would be seriously counterproductive. The loser here would be Delaware, where most large corporations are incorporated. Not only is the debtor’s state of domicile an obvious filing location for a large corporation, but substantial empirical evidence suggests that debtors that file for bankruptcy in Delaware file there because of the expertise of Delaware’s bankruptcy judges.15
Third party releases
Another contentious practice is so-called “third party releases.” When a corporation completes a Chapter 11 reorganization, its prebankruptcy obligations are extinguished. The bankruptcy laws only contemplate that the corporate debtor’s obligations will be extinguished, however, not the obligations of other parties such as the directors or officers of the debtor or outside parties that were involved in wrongdoing by the debtor. In many cases, a corporate debtor asks the court to extinguish the obligations of some of these other parties, often in return for a payment by the third parties. In the Purdue Pharma case, the Sacklers agreed to pay roughly $4.5 billion in return for a court order extinguishing their potential liability related to the opioid crisis. When companies owned by private equity funds file for bankruptcy, the private equity sponsor often seeks this protection. Such a release is known as a third-party release.
Courts have struggled with the question of whether third party releases should be permitted. Except with corporate debtors that have asbestos liability, which are subject to a special rule,16 bankruptcy law does not speak to the question of whether third party releases are permissible. There are plausible arguments that they are constitutional and plausible arguments that they are not.17 Some courts allow them, while others do not. As a result, corporate debtors sometimes seek to file their case in a location where third-party releases are permitted.
The Sacklers’ efforts to obtain third party releases has triggered populist ire at their use. The bankruptcy judge approved the releases, although he required the debtor to reduce the scope of the releases. The district court subsequently reversed, concluding that the bankruptcy laws do not authorize third party releases.18 This decision has been appealed to the federal court of appeals.
As with bankruptcy venue, Congress is currently considering a dramatic intervention—legislation that would almost completely ban third party releases.19 Unlike with venue, there is a plausible argument for simply disallowing third party releases, even if they are legally permissible. The argument is that parties who have not themselves filed for bankruptcy should not be entitled to benefits of bankruptcy such as the extinguishing of debts. If the Sacklers or other third parties want this benefit, they need to file for bankruptcy.
The argument that third party releases should be permitted, at least on some occasions, is more pragmatic. Some argue that the treatment of nondebtors such as the Sacklers is so closely related to the debtor’s reorganization that the company’s financial distress cannot be resolved without also addressing potential claims against the nondebtors.20 Defenders of third party releases also contend that everyone, including victims, may be better off when a release is given in return for compensation by the third parties. The Sacklers have argued that if they were not given relief they would defend themselves vigorously outside of bankruptcy and victims would likely receive much less than the $4.5 billion the Sacklers have agreed to pay in the bankruptcy.
Rather than simply banning third party releases, a more nuanced response would be to insist that third parties seeking a release provide more transparency about their assets and ability to contribute.21 In a sense, they would be required to submit to some same rules about disclosure that would apply if they had filed for bankruptcy. Releases might also be limited to third parties that did in fact make a substantial contribution to the payment of victims or other creditors.
The “Texas Two-Step”
A third controversial practice is moving assets from one entity to another—often creating a “good company” with plenty of assets and an asset poor “bad company”—and then subsequently putting one or both of the entities in bankruptcy. Private equity funds often conduct internal reorganizations that are alleged to have this effect after they acquire a company, as in the Chapter 11 cases of the Chicago Tribune and Caesar’s.22 More recently, financially distressed debtors have taken advantage of a Texas law that appears to bless these transactions.23 The most controversial current example is Johnson & Johnson. Johnson & Johnson created a separate entity for its talc line of business, which is subject to numerous lawsuits, and put the separate entity into bankruptcy. This strategy has become known as the “Texas Two-Step.”
These transactions also have spurred populist backlash, both because they seem to involve manipulation by insiders and because the manipulators often are private equity funds, a bête noire of many populists. The proposed legislation to ban third party releases mentioned earlier also would amend bankruptcy law to require dismissal of any case involving a divisional merger that “had the intent or foreseeable effect of … separating material assets from material liabilities … and … assigning all or a substantial portion of those liabilities to the debtor.”24
As with the other issues, courts already have a more nuanced response available to them. When a company transfers assets from a “bad company” to a “good company” within its corporate structure and one or both later end up in bankruptcy, the transfer can be challenged as a “fraudulent conveyance” if the bad company did not receive adequate compensation for the assets it transferred. Fraudulent conveyance challenges were central to the Chicago Tribune and Caesar’s cases.
With a Texas Two-Step transaction, creditors also can challenge the bankruptcy case as having been filed in bad faith. If the transaction is abusive—if the bad company doesn’t have any real assets, for instance—the court can simply throw the case out.
Lender control of bankruptcy outcomes
Another controversial feature of current practice is lenders’ use of their financing agreement and related contracts to dictate the outcome of a Chapter 11 case. When Neiman Marcus filed for bankruptcy, it had signed a financing agreement with lenders to borrow $675 million, together with a so-called Restructuring Support Agreement that locked in a reorganization plan that required Neiman to transfer control to the lenders.25 Once the financing was approved, the case was over—no other outcome was possible.
If the market for providing financing to debtors in bankruptcy were competitive, lenders’ use of lending agreements to control the restructuring process might be less problematic. But the debtors’ current senior lenders have a monopoly, or nearly so, because other lenders fear that their loan will simply subsidize the senior lenders if the senior lenders have priority over the new lenders. Only if the court awards new lenders a “priming lien”—that is, priority over the current senior lenders—will new lenders offer to finance the debtor’s operations in bankruptcy.  Bankruptcy courts have the power to provide priming liens if the senior lenders will be “adequately protected,” but they have been reluctant to do so.26
Although the monopoly of debtors’ current lenders has not yet gotten significant attention in policy circles, the issue is even more pervasive in practice. As with the issues discussed earlier, the problem does not require a legislative solution. Bankruptcy courts could facilitate competition by signaling a greater willingness to grant priming liens to new lenders and by declining to enforce contractual provisions that impede competition.27
A breaking point?
Complaints about insider control of Chapter 11 were rising even before the recent pandemic. The pressure has steadily increased during the pandemic, due both to the pandemic and to the confluence of highly controversial bankruptcy filings by Purdue Pharma, USA Gymnastics, the Boy Scouts, and others.
The long-term implications of the populist backlash triggered by these developments may depend on how bankruptcy professionals and bankruptcy judges respond to this unrest. If courts address the legitimate concerns raised by bankruptcy populists, the credibility and effectiveness of Chapter 11 may be restored. The Johnson & Johnson and Purdue Sackler cases offer hints of such a trend. With the talc entity of Johnson & Johnson, a bankruptcy judge transferred the case from North Carolina to New Jersey after allegations of forum shopping, and a motion to dismiss the case as having been filed in bad faith is pending. In Purdue Pharma, a district court struck down the controversial Sackler releases.
If these problems continue to fester, the populist backlash may lead to sweeping bankruptcy reform. Such reform is unlikely to be carefully tailored to the problems that prompted it. It could even destroy traditional Chapter 11 practice, much as the Chandler Act of 1938 brought an end to the reorganization framework that presaged current Chapter 11.
Although the pandemic did not overwhelm the bankruptcy system as many expected, it did bring a spate of preexisting conditions to light.28 The lesson for bankruptcy insiders, the “haves” of the bankruptcy process, seems to be “Physician, heal thyself,” before it’s too late.

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Who should regulate: Chairs or majorities of the board

2021 ended with a mini-crisis at the Federal Deposit Insurance Corporation (FDIC) resulting in the chair resigning after being outvoted by a majority of the board of directors. While this fight received substantial press attention, a similar fight occurred at the National Credit Union Administration (NCUA) where a majority of its board overruled its chair. These incidents highlight how financial regulators’ ability to function in the current politically polarized environment can depend on the agencies regulatory structure. A careful examination of recent trends shows trouble on the horizon for regulators of all shapes and sizes.

Aaron Klein

Senior Fellow – Economic Studies


The threat is not just messy politics regarding boards. The ability of single-headed agencies to remain independent of the electoral swings is in doubt. Two recent Supreme Court decisions—featuring the new conservative majority—struck down provisions of two major laws that emerged from the last financial crisis which aimed at creating stronger regulations to address consumer financial protection (CFPB case) and the government-sponsored housing finance agencies (FHFA case). The Court effectively turned the head of each agency into an at-will appointee easily removed by any sitting president, overturning Congress’ intent to create agency heads serving fixed terms meant to provide independence from easy removal by the president.  The ironic result—a conservative court removing Trump-appointed regulators—may provide short-term comfort to pro-regulatory progressives. But the longer-term ramifications of the Court’s rulings effectively curtail political independence of single agency headed regulators, which should give pause to progressives. Given the long time periods required to implement financial regulation and the political difficulties inherent in regulation that gave rise to the desire for agency independence, the end result of the Court’s ruling will probably be deregulatory.
The ability of financial regulators to be independent of the president and to function effectively is at stake, and it is not clear when, how, or what the final outcome will be. An advantage of the board structure was supposed to be consensus-driven policy subject to majority rule. An advantage of a single agency head with a long term who is non-removable by the president was supposed to be the ability of an agency to achieve politically-difficult regulatory outcomes. Both objectives are threatened by recent changes.
Some background for those not steeped in America’s byzantine and bifurcated financial regulatory system. Financial regulators come in all shapes and sizes: single agency heads to seven-member boards; no partisan affiliation to strict partisan splits; boards consisting of people chosen specifically for that purpose to boards that consist of officials from other agencies. The chart below shows the various structures of financial regulators with the footnote containing the full name of each of the alphabet soup of regulators.1
Financial Regulators by Agency Structure

Agency Structure
Single Head
3-Member Board
5-Member Board
7-Member Board


Federal Reserve Board of Governors

Financial regulators are designed to have a greater level of independence from both the executive and legislative branches of government to avoid political interference and more effectively carry out their statutory missions. Each of the agencies mentioned has some independence, with details varying (for more details see this in-depth Congressional Research Service report). Part of this independence involves serving fixed terms ranging from five to 14 years that outlast any specific four-year presidential term. Single agency heads, however, find themselves in a different position. The comptroller is subject to removal by the president for “reasons to be communicated to the Senate,” and two recent Supreme Court decisions struck down provisions of the laws creating the CFPB and FHFA, making each agency head subject to removal at will.
Boards vs. single agency heads
Scholars debate how to balance regulatory independence with public accountability, including how much regulatory structure between boards and single agency heads matters. Congress and presidents of both parties have recently preferred single agency heads as opposed to boards. Financial regulatory agencies are usually created in response to financial crises, and the three most recently created financial regulators, FHFA, CFPB, and OFR, were all created in response to the 2008 crisis. Each was given a single agency head with terms that extend beyond the tenure of a presidential administration and limits placed on their removal by future presidents.  The laws that created the three were signed by Presidents Bush 43 (FHFA) and Obama (CFPB and OFR) and had varying degrees of bipartisan support. While this structure was popular, it has been transmuted by these Supreme Court cases into agencies whose heads serve at the will of the president and can be quickly removed and replaced if they are at odds with the White House.
Boards were the more common structure for financial regulators created in the 20th century. Proponents of boards argue that by bringing multiple perspectives, often with bipartisan requirements, agreements can be worked out between different political and regulatory philosophies by non-elected but appointed individuals. Term appointments that cannot be rescinded at will provide board members the autonomy to form agreements not supported by elected political officials.
Different rules govern the role and designation process for the chair between the regulators. For the Federal Reserve, the position and term of chairmanship is separable from a position on the Board. This distinction between chair and Fed Board member came into play when President Trump publicly contemplated firing Fed Chairman Powell. The law was clear that Trump could not remove Powell from the Fed Board, but less clear as to whether Trump could remove Powell from the chairmanship. This was important as the Fed’s monetary policy arm, the Federal Open Market Committee (FOMC), elects its own chair and could in theory have kept Powell as chairman of the FOMC regardless of any attempt by Trump to demote his standing at the Board. Thus, the manner in which regulatory boards are structured can enhance independence.
Bank regulatory boards: Majority rule?
The FDIC and NCUA currently have chairs who are in the political and policy minority. The FDIC has a majority of Democratic members converging to overrule the Republican chair. Their first action was a request for public comment regarding bank mergers and acquisitions. The FDIC chair disputed the ability of the majority of the agency to take action without her consent. The regulatory action taken, inviting comment for a review of bank merger regulation, is consistent with the priorities of the Biden administration as the White House encouraged the FDIC to update their merger guidelines in an executive order issued in July 2021.
The FDIC action is further complicated by the chair’s position that no action has been taken because she has not agreed to place the item on the agenda for a formal vote. The other FDIC board members take the position that they have voted on this topic with one board member placing the request for comment on his agency’s website. That these conflicting actions can occur is possible because, uniquely among financial regulators, the FDIC Board contains two heads of other agencies (CFPB and OCC) as members. These agency heads have access to post actions in the Federal Register and on their own websites in a manner that other agencies’ non-chair members do not necessarily have. The FDIC Chair announced her resignation on New Year’s Eve, defusing the current crisis. However, had she not chosen to resign, then either the situation would have ended up in the courts or the president would have had to try to remove her. Either would have been a messy situation, leading to a period where basic operation and control of the institution insuring American’s bank accounts was in fundamental doubt.
The ability of financial regulators to be independent of the president and to function effectively is at stake.
In contrast, the NCUA has seen the two non-chair Republican board members outvote the Democratic chairman to pursue a deregulatory agenda, approving rules deregulating lending requirements on so-called payday alternatives and activities of service organizations affiliated with the credit union. This deregulation is consistent with the conservative ideology of the Republican Trump appointees who have worked together to override the position of the Democratic appointee whom President Biden designated as chairman.
Two financial regulators are pursuing two opposite paths consistent with the standard political positions among the majority of their boards. The NCUA is deregulating while the FDIC is starting down the path to likely increase regulatory thresholds. The NCUA’s actions have earned the praise of credit union industry advocates while the FDIC’s actions have drawn rebukes by industry. Industry supporting deregulation and opposing regulation is not surprising, but it is important to note the role process plays in the debate. Praise of NCUA focuses on the actions of the majority of the board, while opposition to the FDIC focuses on the process. This is important because the method for a chair to avoid being overruled is to manipulate the process such that the board never has an opportunity to vote on an item. Control of the process can equate to control of the agency, regardless of the will of the majority.
Boards are expected to operate under majority rule, creating the potential for a board chair to be in the minority. This is what happened at the NCUA, which garnered little fanfare or media attention. It is not unique to the NCUA. While the Federal Reserve Board operated recently under substantial consensus, this was not always the case. The late Fed Chairman Paul Volcker was outvoted 4-3 on an issue before the Fed Board of Governors in the 1980s, as he detailed in his memoir. More recently, SEC Chairman Donaldson, a Bush appointee, joined with Democratic commissioners to pass a rule regarding mutual funds on a 3-2 vote. Regardless of the politics of the members involved, the principle of majority rule has held.
Should a chair be able to block a majority, the threshold for regulatory action becomes even greater. A five-member board needs both a majority (three members) and the chair. If FDIC Chair McWilliams had been able to set this precedent, it would raise another challenge for the FDIC to act going forward given that: two of the five members are heads of other agencies; that of the three FDIC full-time board members two are frequently of the minority party (in order to satisfy the no more than three members of the same party split required); and the natural hurdle of giving one member greater power to block action than to allow it.
Regulators run by a single agency head inherently escape the dilemma between the chair and the will of the majority of a board. Yet a single agency head that is not structurally independent of the sitting president is easily removed every time the White House flips. That is the situation the new financial regulators find themselves in after the Supreme Court’s two rulings mentioned earlier. The Supreme Court has checked the desire of Congress and presidents of both parties to structure new financial regulators as agencies headed by single individuals with this level of independence. This check had immediate ramifications. President Biden changed CFPB directors on day one of his presidency, a big contrast from his general lack of nominations to other bank regulatory boards (by December 20, 2021 Biden had not nominated a single new person to the Federal Reserve Board of Governors or the FDIC). Biden removed the FHFA director immediately after the Supreme Court issued its ruling on that case.

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The long-term result of the Court’s actions will be regulators that are less independent and more politically controlled by the White House. Chief Justice Roberts’ ruling distinguishes between multi-member boards and single agency heads in finding differences as to the constitutionality of non-at-will appointments. Absent a change in the Supreme Court’s view of these issues, the creation of single agency head regulators will be confined to those easily replaced by presidents.  One may disagree with this logic, but until reversed, it stands. The result will be a greater incentive for Congress to structure financial regulators as boards for greater independence from the executive branch.
2021 featured a credit union regulator with a board overriding the chair and a bank regulator in a battle with a chair who views the majority as attempting to “wrest control from an independent agency’s chairman with a change in the administration.” The FDIC chair’s resignation sets 2022 off in a new direction, defusing the immediate crisis, but the structural problems of both models of financial regulation—boards and single agency heads—still need to be addressed.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.

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Opening statement of Aaron Klein at roundtable on America’s unbanked and underbanked

Chairman Himes, Ranking Member Steil, members of the Committee, thank you for inviting me to participate at this roundtable on America’s unbanked and underbanked. I laud the Committee’s attention to these substantial problems that impact approximately one out of four American families. Rectifying these problems is essential to achieving this Committee’s goal of addressing the growing prosperity gap.

Aaron Klein

Senior Fellow – Economic Studies


Basic financial services have become a reverse Robin Hood system whereby lower income Americans pay tens of billions for services that middle- and upper-income Americans receive for free.1 This is particularly the case for the underbanked. The Federal Deposit Insurance Corporation(FDIC) estimates that about one in six American households are underbanked, which they define as using a bank account but also using a payday lender, check casher, or wire transmitter service.2 In addition, one in twelve American families with bank accounts pay $350 a year or more in overdraft fees, according to the Consumer Financial Protection Bureau.3
Why are so many Americans paying so much for financial services when they have a bank account? The answer: our financial system is not well structured to provide the services people living paycheck-to-paycheck need at low costs. This forces many people into high cost workarounds. The result is that the less money you have the more money you spend to access your own money. Basic banking is one reason why it is expensive to be poor in America.
An example elucidates this problem. Consider depositing a check. Despite rapid improvements in the technology involved in clearing checks and legislative changes to enable this new technology (the Check 21 Act of 2004, which I worked on) it still takes sometimes as long as six days for the money from a check to be available in a consumer’s account. For those who always have money in the bank, this delay is relatively meaningless. But for those who are living paycheck to paycheck the results are devastating.
Consumers who run out of money while waiting for the payment to clear are left with bleak options: continue to pay bills and use your debit card, you face overdraft fees that average $35 per transaction; go to a payday lender and pay $50 for a few hundred dollars to make it through the weekend; or avoid the bank altogether and take your check to a check casher, paying on average $20 and get cash immediately.
Estimates for the total amount of overdraft fees paid range from $15 to $35 billion a year.4 5 Payday lending fees have been estimated at nearly $10 billion.6 These are fees only paid by people with bank accounts—by definition every overdraft fee is paid to a bank or credit union and every payday loan requires an account in order to give the lender a post-dated check as collateral. My research using FDIC data shows that 70 percent of check-cashing customers have bank accounts and that the majority of checks cashed are from these customers.7

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Other elements of our slow payment system drive up these costs, including banks and credit unions’ ability to post debits before credits and the inability for consumers to even know when exactly their check or direct deposit will be available. Millions of Americans got paid on Friday, December 10. For most with so-called ‘direct deposit,’ the money was actually withdrawn from their employer several days earlier. This money has been sitting for days while people living paycheck-to-paycheck are spending potentially $50 billion a year in just these three fees alone as they wait for their own money to arrive.
This helps explain the main reason why people without a bank account report not having one: Bank accounts are too expensive and do not provide the service they need. Roughly half of the unbanked cite costs and fees as the main reason why they do not have a bank account.8 In comparison, less than one in twenty cite location or hours as the main reason. Far too many solutions to the ‘unbanked problem’ focus on questions of physical access when the main problems are cost and speed.
The problems in our banking and payment system impact federal government programs, reducing the effectiveness of policies meant to help. For example, Congress acted with incredible alacrity in providing emergency assistance to millions of American families who were suddenly without income at the beginning of the COVID-19 pandemic, enacting emergency payments to families just weeks after the shutdown hit on March 27, 2020. However, the U.S. Treasury did not start sending out money until April 10, and then it took another five days until April 15 before the funds were actually available to those who were lucky enough to receive the first batch. What were families supposed to do for those days while they waited for their money?
Less than half of all eligible Americans received their COVID-19 stimulus in that first round. More than one-third had to wait until May or later to receive their emergency funds.9 And when they did receive their money, for one in four it was by paper check or plastic card. How can it be that 95 percent of families in this country have a bank account, but Uncle Sam could not find 25 percent of Americans bank accounts to give them money in the midst of a national pandemic? Part of the reason is that the Treasury Department simply does not have the information, and another part is that they are unwilling and unable to work with those private sector companies that do.10 This problem still has not been solved, as one in seven families eligible for the child tax credit did not receive their money through direct deposit.11
The result is meaningful for those impacted. One estimate is that $66 million of the first round of CARES Act stimulus payments went to check cashers, as people couldn’t afford to continue waiting. There is still not an easy system for families who are receiving the child tax credit to use it as the regular direct deposit that is required by many banks and credit unions to be eligible for ‘free checking’ accounts.
There are several simple and hopefully bipartisan solutions to these problems. The single most impactful thing the federal government could do is to give people access to their own money immediately. This can be done by simply amending the Expedited Funds Availability Act to require immediate access for the first several thousand dollars of a deposit, instead of permitting the lengthy, costly delays that harm people living paycheck to paycheck. Empowering people to have access to their own money immediately ought to be the small ‘c’ conservative idea that crosses ideologies and forms sensible policy.
Access to digital money is a requirement to participate in the new digital economy.  Accessing digital money is easy and free for those with money while for those without a lot of money, digital money is expensive. Requiring all banks to offer a low-cost, basic bank account is one solution to many aspects of this problem.12 The FDIC designed a Safe Account product, which has been picked up by the BankOn movement. Many banks and credit unions offer these types of accounts already. The American Bankers Association urges banks to offer such an account as part of its best practices.13 This best practice should be universal so that any American in any bank can open a basic low-cost, full-service account. Every bank and credit union has a charter from the government. That charter provides great benefits and also responsibilities. A basic, low-cost account is such a responsibility.
In conclusion, there are no magical, single-bullet solutions to fix the entire system. But there are a series of simple policy levers that can be pulled, each of which helps to fix a portion of the problem. Thank you very much for the opportunity to participate, and I look forward to engaging in a lively conversation.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.

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An AI fair lending policy agenda for the federal financial regulators

Algorithms, including artificial intelligence and machine learning models (AI/ML), increasingly dictate many core aspects of everyday life. Whether applying for a job or a loan, renting an apartment, or seeking insurance coverage, AI-powered statistical models decide who will have access to the foundational drivers of opportunity and equality.1

Michael Akinwumi

Chief Tech Equity Officer – National Fair Housing Alliance


John Merrill

Chief Technology Officer – FairPlay AI


Lisa Rice

President and CEO – National Fair Housing Alliance


Kareem Saleh

Founder & CEO – FairPlay AI


Maureen Yap

Senior Counsel – National Fair Housing Alliance

These models present both great promise and great risk. They can minimize human subjectivity and bias, facilitate more consistent outcomes, increase efficiencies, and generate more accurate decisions. Properly conceived and managed, algorithmic, and AI-based systems can be opportunity-expanding. At the same time, a variety of factors—including data limitations, lack of diversity in the technology field, and a long history of systemic inequality in America—mean that algorithmic decisions can perpetuate discrimination against historically underserved groups, such as people of color and women.
In light of the growing adoption of AI/ML, federal regulators—including the Consumer Financial Protection Bureau (CFPB), Federal Trade Commission (FTC), the Department of Housing and Urban Development (HUD), Office of the Comptroller of the Currency (OCC), Board of Governors of the Federal Reserve (Federal Reserve), Federal Deposit Insurance Corporation (FDIC), and National Credit Union Administration (NCUA)—have been evaluating how existing laws, regulations, and guidance should be updated to account for the advent of AI in consumer finance. Earlier this year some of these regulators issued a request for information on financial institutions’ use of AI and machine learning in the areas of fair lending, cybersecurity, risk management, credit decisions, and other areas.2
The adoption of responsible AI/ML policies will continue to receive serious attention from regulators. This paper proposes policy and enforcement steps regulators can take to ensure AI/ML is harnessed to advance financial inclusion and fairness. As many other papers have already focused on methods for embracing the benefits of AI, we focus here on providing recommendations to regulators on how to identify and control for the risks in order to build an equitable market.
I. Background
A. AI/ML and consumer finance
For decades, lenders have used models and algorithms to make credit-related decisions, the most obvious examples being credit underwriting and pricing. Today, models are ubiquitous in consumer markets and are constantly being applied in new ways, such as marketing, customer relations, servicing, and default management. Lenders also commonly rely on models and modeled variables provided by third-party vendors.
Recent increases in computing power and exponential growth in available data have spurred the advancement of even more sophisticated statistical techniques. In particular, entities are increasingly using AI/ML, which involves exposing sophisticated algorithms to historical “training” data to discover complex correlations or relationships between variables in a dataset.  The set of discovered relationships—typically referred to as a “model”—is then run against real-world information to predict future outcomes.
In the consumer finance context, AI/ML is similar to traditional forms of statistical analysis in that both are used to identify patterns in historical data to draw inferences and future behavior.  What makes AI/ML unique is the ability to analyze much larger amounts of data and discover complex relationships between numerous data points that would normally go undetected by traditional statistical analysis. AI/ML tools are also capable of adapting to new information—or “learning”—without human intervention. These tools are becoming increasingly popular in both the private and public sectors. As two United States senators recently put it, “algorithms are increasingly embedded into every aspect of modern society.”3
B. The risks posed by AI/ML in consumer finance
While AI/ML models offer benefits, they also have the potential to perpetuate, amplify, and accelerate historical patterns of discrimination. For centuries, laws and policies enacted to create land, housing, and credit opportunities were race-based, denying critical opportunities to Black, Latino, Asian, and Native American individuals. Despite our founding principles of liberty and justice for all, these policies were developed and implemented in a racially discriminatory manner. Federal laws and policies created residential segregation, the dual credit market, institutionalized redlining, and other structural barriers. Families that received opportunities through prior federal investments in housing are some of America’s most economically secure citizens. For them, the nation’s housing policies served as a foundation of their financial stability and the pathway to future progress. Those who did not benefit from equitable federal investments in housing continue to be excluded.
Algorithmic systems often have disproportionately negative effects on people and communities of color, particularly with respect to credit, because they reflect the dual credit market that resulted from our country’s long history of discrimination.4 This risk is heightened by the aspects of AI/ML models that make them unique: the ability to use vast amounts of data, the ability to discover complex relationships between seemingly unrelated variables, and the fact that it can be difficult or impossible to understand how these models reach conclusions. Because models are trained on historical data that reflect and detect existing discriminatory patterns or biases, their outputs will reflect and perpetuate those same problems.5

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Examples of discriminatory models abound, particularly in the finance and housing space. In the housing context, tenant screening algorithms offered by consumer reporting agencies have had serious discriminatory effects.6 Credit scoring systems have been found to discriminate against people of color.7 Recent research has raised concerns about the connection between Fannie Mae and Freddie Mac’s use of automated underwriting systems and the Classic FICO credit score model and the disproportionate denials of home loans for Black and Latino borrowers.8
These examples are not surprising because the financial industry has for centuries excluded people and communities from mainstream, affordable credit based on race and national origin.9 There has never been a time when people of color have had full and fair access to mainstream financial services. This is in part due to the separate and unequal financial services landscape, in which mainstream creditors are concentrated in predominantly white communities and non-traditional, higher-cost lenders, such as payday lenders, check cashers, and title money lenders, are hyper-concentrated in predominantly Black and Latino communities.10
Communities of color have been presented with unnecessarily limited choices in lending products, and many of the products that have been made available to these communities have been designed to fail those borrowers, resulting in devastating defaults.11 For example, borrowers of color with high credit scores have been steered into subprime mortgages, even when they qualified for prime credit.12 Models trained on this historic data will reflect and perpetuate the discriminatory steering that led to disproportionate defaults by borrowers of color.13
Biased feedback loops can also drive unfair outcomes by amplifying discriminatory information within the AI/ML system. For example, a consumer who lives in a segregated community that is also a credit desert might access credit from a payday lender because that is the only creditor in her community. However, even when the consumer pays off the debt on time, her positive payments will not be reported to a credit repository, and she loses out on any boost she might have received from having a history of timely payments. With a lower credit score, she will become the target of finance lenders who peddle credit offers to her.14 When she accepts an offer from the finance lender, her credit score is further dinged because of the type of credit she accessed. Thus, living in a credit desert prompts accessing credit from one fringe lender that creates biased feedback that attracts more fringe lenders, resulting in a lowered credit score and further barriers to accessing credit in the financial mainstream.
In all these ways and more, models can have a serious discriminatory impact. As the use and sophistication of models increases, so does the risk of discrimination.
C. The applicable legal framework
In the consumer finance context, the potential for algorithms and AI to discriminate implicates two main statutes: the Equal Credit Opportunity Act (ECOA) and the Fair Housing Act. ECOA prohibits creditors from discriminating in any aspect of a credit transaction on the basis of race, color, religion, national origin, sex, marital status, age, receipt of income from any public assistance program, or because a person has exercised legal rights under the ECOA.15  The Fair Housing Act prohibits discrimination in the sale or rental of housing, as well as mortgage discrimination, on the basis of race, color, religion, sex, handicap, familial status, or national origin.16
ECOA and the Fair Housing Act both ban two types of discrimination: “disparate treatment” and “disparate impact.”  Disparate treatment is the act of intentionally treating someone differently on a prohibited basis (e.g., because of their race, sex, religion, etc.). With models, disparate treatment can occur at the input or design stage, for example by incorporating a prohibited basis (such as race or sex) or a close proxy for a prohibited basis as a factor in a model. Unlike disparate treatment, disparate impact does not require intent to discriminate.  Disparate impact occurs when a facially neutral policy has a disproportionately adverse effect on a prohibited basis, and the policy either is not necessary to advance a legitimate business interest or that interest could be achieved in a less discriminatory way.17
II. Recommendations for mitigating AI/ML Risks
In some respects, the U.S. federal financial regulators are behind in advancing non-discriminatory and equitable technology for financial services.18 Moreover, the propensity of AI decision-making to automate and exacerbate historical prejudice and disadvantage, together with its imprimatur of truth and its ever-expanding use for life-altering decisions, makes discriminatory AI one of the defining civil rights issues of our time. Acting now to minimize harm from existing technologies and taking the necessary steps to ensure all AI systems generate non-discriminatory and equitable outcomes will create a stronger and more just economy.
The transition from incumbent models to AI-based systems presents an important opportunity to address what is wrong in the status quo—baked-in disparate impact and a limited view of the recourse for consumers who are harmed by current practices—and to rethink appropriate guardrails to promote a safe, fair, and inclusive financial sector. The federal financial regulators have an opportunity to rethink comprehensively how they regulate key decisions that determine who has access to financial services and on what terms. It is critically important for regulators to use all the tools at their disposal to ensure that institutions do not use AI-based systems in ways that reproduce historical discrimination and injustice.
A. Set clear expectations for best practices in fair lending testing, including a rigorous search for less discriminatory alternatives
Existing civil rights laws and policies provide a framework for financial institutions to analyze fair lending risk in AI/ML and for regulators to engage in supervisory or enforcement actions, where appropriate. However, because of the ever-expanding role of AI/ML in consumer finance and because using AI/ML and other advanced algorithms to make credit decisions is high-risk, additional guidance is needed. Regulatory guidance that is tailored to model development and testing would be an important step towards mitigating the fair lending risks posed by AI/ML.
Below we propose several measures that would mitigate those risks.
1. Set clear and robust regulatory expectations regarding fair lending testing to ensure AI models are non-discriminatory and equitable 
Federal financial regulators can be more effective in ensuring compliance with fair lending laws by setting clear and robust regulatory expectations regarding fair lending testing to ensure AI models are non-discriminatory and equitable. At this time, for many lenders, the model development process simply attempts to ensure fairness by (1) removing protected class characteristics and (2) removing variables that could serve as proxies for protected class membership. This type of review is only a minimum baseline for ensuring fair lending compliance, but even this review is not uniform across market players. Consumer finance now encompasses a variety of non-bank market players—such as data providers, third-party modelers, and financial technology firms (fintechs)—that lack the history of supervision and compliance management. They may be less familiar with the full scope of their fair lending obligations and may lack the controls to manage the risk. At a minimum, the federal financial regulators should ensure that all entities are excluding protected class characteristics and proxies as model inputs.19
Removing these variables, however, is not sufficient to eliminate discrimination and comply with fair lending laws. As explained, algorithmic decisioning systems can also drive disparate impact, which can (and does) occur even absent using protected class or proxy variables. Guidance should set the expectation that high-risk models—i.e., models that can have a significant impact on the consumer, such as models associated with credit decisions—will be evaluated and tested for disparate impact on a prohibited basis at each stage of the model development cycle.
Despite the need for greater certainty, regulators have not clarified and updated fair lending examination procedures and testing methodologies for several years. As a result, many financial institutions using AI/ML models are uncertain about what methodologies they should use to assess their models and what metrics their models are expected to follow. Regulators can ensure more consistent compliance by explaining the metrics and methodologies they will use for evaluating an AI/ML model’s compliance with fair lending laws.
2. Clarify that the federal financial regulators will conduct a rigorous search for less discriminatory alternatives as part of fair lending examinations, and set expectations that lenders should do the same 
The touchstone of disparate impact law has always been that an entity must adopt an available, less discriminatory alternative (LDA) to a practice that has discriminatory effect, so long as the alternative can satisfy the entity’s legitimate needs. Consistent with this central requirement, responsible financial institutions routinely search for and adopt LDAs when fair lending testing reveals a disparate impact on a prohibited basis. But not all do. In the absence of a robust fair lending compliance framework, the institutions that fail to search for and adopt LDAs will unnecessarily perpetuate discrimination and structural inequality. Private enforcement against these institutions is difficult because outside parties lack the resources and/or transparency to police all models across all lenders.
Given private enforcement challenges, consistent and widespread adoption of LDAs can only happen if the federal financial regulators conduct a rigorous search for LDAs and expect the lenders to do the same as part of a robust compliance management system. Accordingly, regulators should take the following steps to ensure that all financial institutions are complying with this central tenet of disparate impact law:
a. Inform financial institutions that regulators will conduct a rigorous search for LDAs during fair lending examinations so that lenders also feel compelled to search for LDAs to mitigate their legal risk. Also inform financial institutions how regulators will search for LDAs, so that lenders can mirror this process in their own self-assessments.
b. Inform financial institutions that they are expected to conduct a rigorous LDA search as part of a robust compliance management system, and to advance the policy goals of furthering financial inclusion and racial equity.
c. Remind lenders that self-identification and prompt corrective action will receive favorable consideration under the Uniform Interagency Consumer Compliance Rating System20 and the CFPB’s Bulletin on Responsible Business Conduct.21 This would send a signal that self-identifying and correcting likely fair lending violations will be viewed favorably during supervisory and enforcement matters.
The utility of disparate impact and the LDA requirement as a tool for ensuring equal access to credit lies not only in enforcement against existing or past violations but in shaping the ongoing processes by which lenders create and maintain the policies and models they use for credit underwriting and pricing. Taking the foregoing steps would help ensure that innovation increases access to credit without unlawful discrimination.
3. Broaden Model Risk Management Guidance to incorporate fair lending risk
For years, financial regulators like the OCC and Federal Reserve have articulated Model Risk Management (“MRM”) Guidance, which is principally concerned with mitigating financial safety and soundness risks that arise from issues of model design, construction, and quality.22 The MRM Guidance does not account for or articulate principles for guarding against the risks that models cause or the perpetuation of discrimination. Broadening the MRM Guidance scope would ensure institutions are guarding against discrimination risks throughout the model development and use process. In particular, regulators should clearly define “model risk” to include the risk of discriminatory or inequitable outcomes for consumers rather than just the risk of financial loss to a financial institution.
Effective model risk management practices would aid compliance with fair lending laws in several ways. First, model risk management practices can facilitate variable reviews by ensuring institutions understand the quality of data used and can identify potential issues, such as datasets that are over- or under-representative for certain populations. Second, model risk management practices are essential to ensuring that models, and variables used within models, meet a legitimate business purpose by establishing that models meet performance standards to achieve the goals for which they were developed. Third, model risk management practices establish a routine cadence for reviewing model performance. Fair lending reviews should, at a minimum, occur at the same periodic intervals to ensure that models remain effective and are not causing new disparities because of, for example, demographic changes in applicant and borrower populations.
To provide one example of how revising the MRM Guidance would further fair lending objectives, the MRM Guidance instructs that data and information used in a model should be representative of a bank’s portfolio and market conditions.23 As conceived of in the MRM Guidance, the risk associated with unrepresentative data is narrowly limited to issues of financial loss. It does not include the very real risk that unrepresentative data could produce discriminatory outcomes. Regulators should clarify that data should be evaluated to ensure that it is representative of protected classes. Enhancing data representativeness would mitigate the risk of demographic skews in training data being reproduced in model outcomes and causing financial exclusion of certain groups.
One way to enhance data representativeness for protected classes would be to encourage lenders to build models using data from Minority Depository Institutions (MDIs) and Community Development Financial Institutions (CDFIs), which have a history of successfully serving minority and other underserved communities; adding their data to a training dataset would make the dataset more representative. Unfortunately, many MDIs and CDFIs have struggled to report data to consumer reporting agencies in part due to minimum reporting requirements that are difficult for them to satisfy. Regulators should work with both consumer reporting agencies and institutions like MDIs and CDFIs to identify and overcome obstacles to the incorporation of this type of data in mainstream models.
4. Provide guidance on evaluating third-party scores and models
Financial institutions routinely rely on third-party credit scores and models to make major financial decisions. These scores and models often incorporate AI/ML methods. Third-party credit scores and other third-party models can drive discrimination, and there is no basis for immunizing them from fair lending laws. Accordingly, regulators should make clear that fair lending expectations and mitigation measures apply as much to third-party credit scores and models as they do to institutions’ own models.
More specifically, regulators should clarify that, in connection with supervisory examinations, they may conduct rigorous searches for disparate impact and less discriminatory alternatives related to third-party scores and models and expect the lenders to do the same as part of a robust compliance management system. The Federal Reserve Board, FDIC, and OCC recently released the “Proposed Interagency Guidance on Third-Party Relationships: Risk Management,” which states: “When circumstances warrant, the agencies may use their authorities to examine the functions or operations performed by a third party on the banking organization’s behalf. Such examinations may evaluate…the third party’s ability to…comply with applicable laws and regulations, including those related to consumer protection (including with respect to fair lending and unfair or deceptive acts or practices) ….”24  While this guidance is helpful, the regulators can be more effective in ensuring compliance by setting clear, specific, and robust regulatory expectations regarding fair lending testing for third-party scores and models. For example, regulators should clarify that protected class and proxy information should be removed, that credit scores and third-party models should be tested for disparate impact, and that entities are expected to conduct rigorous searches for less discriminatory alternative models as part of a robust compliance management program.25
5. Provide guidance clarifying the appropriate use of AI/ML during purported pre-application screens
Concerns have been raised about the failure to conduct fair lending testing on AI/ML models that are used in purported pre-application screens such as models designed to predict whether a potential customer is attempting to commit fraud. As with underwriting and pricing models, these models raise the risk of discrimination and unnecessary exclusion of applicants on a prohibited basis. Unfortunately, some lenders are using these pre-application screens to artificially limit the applicant pool that is subject to fair lending scrutiny. They do so by excluding from the testing pool those prospective borrowers who were purportedly rejected for so-called “fraud”-based or other reasons rather than credit-related reasons. In some cases, “fraud”26 is even defined as a likelihood that the applicant will not repay the loan—for example, that an applicant may max out a credit line and be unwilling to pay back the debt. This practice can artificially distort the lender’s applicant pool that is subject to fair lending testing and understate denial rates for protected class applicants.
Regulators should clarify that lenders cannot evade civil rights and consumer protection laws by classifying AI/ML models as fraud detection rather than credit models and that any model used to screen out applicants must be subject to the same fair lending monitoring as other models used in the credit process.
B. Provide clear guidance on the use of protected class data to improve credit outcomes
Any disparate impact analysis of credit outcomes requires awareness or estimation of protected class status. It is lawful—and often necessary—for institutions to make protected-class neutral changes to practices (including models) to decrease any outcome disparities observed during fair lending testing. For example, institutions may change decision thresholds or remove or substitute model variables to reduce observed outcome disparities.
Institutions should also actively mitigate bias and discrimination risks during model development. AI/ML researchers are exploring fairness enhancement techniques to be used during model pre-processing and in-processing, and evidence exists that these techniques could significantly improve model fairness. Some of these techniques use protected class data during model training but do not use that information while scoring real-world applications once the model is in production. This raises the question of the ways in which the awareness or use of protected class data during training is permissible under the fair lending laws. If protected class data is being used for a salutary purpose during model training—such as to improve credit outcomes for historically disadvantaged groups—there would seem to be a strong policy rationale for permitting it, but there is no regulatory guidance on this subject. Regulators should provide clear guidance to clarify the permissible use of protected class data at each stage of the model development process in order to encourage developers to seek optimal outcomes whenever possible.
C. Consider improving race and gender imputation methodologies
Fair lending analyses of AI/ML models—as with any fair lending analysis—require some awareness of applicants’ protected class status. In the mortgage context, lenders are permitted to solicit this information, but ECOA and Regulation B prohibit creditors from collecting it from non-mortgage credit applicants. As a result, regulators and industry participants rely on methodologies to estimate the protected class status of non-mortgage credit applicants to test whether their policies and procedures have a disparate impact or result in disparate treatment. The CFPB, for example, uses Bayesian Improved Surname Geocoding (BISG), which is also used by some lenders and other entities.27  BISG can be useful as part of a robust fair lending compliance management system. Using publicly available data on names and geographies, BISG can allow agencies and lenders to improve models and other policies that cause disparities in non-mortgage credit on a prohibited basis.28
Regulators should continue to research ways to further improve protected class status imputation methodologies using additional data sources and more advanced mathematical techniques. Estimating protected class status of non-mortgage credit applicants is only necessary because Regulation B prohibits creditors from collecting such information directly from those applicants.29 The CFPB should consider amending Regulation B to require lenders to collect protected class data as a part of all credit applications, just as they do for mortgage applications.
D. Ensure lenders provide useful adverse action notices
AI/ML explainability for individual decisions is important for generating adverse action reasons in accordance with ECOA and Regulation B.30 Regulation B requires that creditors provide adverse action notices to credit applicants that disclose the principal reasons for denial or adverse action.31 The disclosed reasons must relate to and accurately describe the factors the creditor considered. This requirement is motivated by consumer protection concerns regarding transparency in credit decision making and preventing unlawful discrimination. AI/ML models sometimes have a “black box” quality that makes it difficult to know why a model reached a particular conclusion. Adverse action notices that result from inexplicable AI/ML models are generally not helpful or actionable for the consumer.
Unfortunately, a CFPB blog post regarding the use of AI/ML models when providing adverse action notices seemed to emphasize the “flexibility” of the regulation rather than ensuring that AI providers and users adhere to the letter and spirit of ECOA, which was meant to ensure that consumers could understand the credit denials that impact their lives.32 The complications raised by AI/ML models do not relieve creditors of their obligations to provide reasons that “relate to and accurately describe the factors actually considered or scored by a creditor.”[33] Accordingly, the CFPB should make clear that creditors using AI/ML models must be able to generate adverse action notices that reliably produce consistent, specific reasons that consumers can understand and respond to, as appropriate. As the OCC has emphasized, addressing fair lending risks requires an effective explanation or explainability method; regardless of the model type used: “bank management should be able to explain and defend underwriting and modeling decisions.”34
There is little current emphasis in Regulation B on ensuring these notices are consumer-friendly or useful. Creditors treat them as formalities and rarely design them to actually assist consumers.  As a result, adverse action notices often fail to achieve their purpose of informing consumers why they were denied credit and how they can improve the likelihood of being approved for a similar loan in the future. This concern is exacerbated as models and data become more complicated and interactions between variables less intuitive.
The model adverse action notice contained in Regulation B illustrates how adverse action notices often fail to meaningfully assist consumers. For instance, the model notice includes vague reasons, such as “Limited Credit Experience.” Although this could be an accurate statement of a denial reason, it does not guide consumer behavior. An adverse action notice that instead states, for example, you have limited credit experience; consider using a credit-building product, such as a secured loan, or getting a co-signer, would provide better guidance to the consumer about how to overcome the denial reason. Similarly, the model notice in Regulation B includes “number of recent inquiries on credit bureau report” as a sample denial reason. This denial reason may not be useful because it does not provide information about directionality. To ensure that adverse action notices are fulfilling their statutory purpose, the CFPB should require lenders to provide directionality associated with principal reasons and explore requiring lenders to provide notices containing counterfactuals—the changes the consumer could make that would most significantly improve their chances of receiving credit in the future.
E. Engage in robust supervision and enforcement activities
Regulators should ensure that financial institutions have appropriate compliance management systems that effectively identify and control risks related to AI/ML systems, including the risk of discriminatory or inequitable outcomes for consumers. This approach is consistent with the Uniform Interagency Consumer Compliance Rating System35 and the Model Risk Management Guidance. The compliance management system should comprehensively cover the roles of board and senior management, policies and procedures, training, monitoring, and consumer complaint resolution. The extent and sophistication of the financial institution’s compliance management system should align with the extent, sophistication, and risk associated with the financial institution’s usage of the AI system, including the risk that the AI system could amplify historical patterns of discrimination in financial services.
Where a financial institution’s use of AI indicates weaknesses in their compliance management system or violations of law, the regulators should use all the tools at their disposal to quickly address and prevent consumer harm, including issuing Matters Requiring Attention; entering into a non-public enforcement action, such as a Memorandum of Understanding; referring a pattern or practice of discrimination to the U.S. Department of Justice; or entering into a public enforcement action. The Agencies have already provided clear guidance (e.g., the Uniform Consumer Compliance Rating System) that financial institutions must appropriately identify, monitor, and address compliance risks, and the regulators should not hesitate to act within the scope of their authority. When possible, the regulators should explain to the public the risks that they have observed and the actions taken in order to bolster the public’s trust in robust oversight and provide clear examples to guide the industry.
F. Release additional data and encourage public research
Researchers and advocacy groups have made immense strides in recent years studying discrimination and models, but these efforts are stymied by a lack of publicly available data. At present, the CFPB and the Federal Housing Finance Agency (FHFA) release some loan-level data through the National Survey of Mortgage Originations (NSMO) and Home Mortgage Disclosure Act (HMDA) databases. However, the data released into these databases is either too limited or too narrow for AI/ML techniques truly to discern how current underwriting and pricing practices could be fairer and more inclusive. For example, there are only about 30,000 records in NSMO, and HMDA does not include performance data or credit scores.
Adding more records to the NSMO database and releasing additional fields in the HMDA database (including credit score) would help researchers and advocacy groups better understand the effectiveness of various AI fairness techniques for underwriting and pricing. Regulators also should consider how to expand these databases to include more detailed data about inquiries, applications, and loan performance after origination. To address any privacy concerns, regulators could implement various measures such as only making detailed inquiry and loan-level information (including non-public HMDA data) available to trusted researchers and advocacy groups under special restrictions designed to protect consumers’ privacy rights.
In addition, NSMO and HMDA both are limited to data on mortgage lending. There are no publicly available application-level datasets for other common credit products such as credit cards or auto loans. The absence of datasets for these products precludes researchers and advocacy groups from developing techniques to increase their inclusiveness, including through the use of AI. Lawmakers and regulators should therefore explore the creation of databases that contain key information on non-mortgage credit products. As with mortgages, regulators should evaluate whether inquiry, application, and loan performance data could be made publicly available for these credit products.
Finally, the regulators should encourage and support public research. This support could include funding or issuing research papers, convening conferences involving researchers, advocates, and industry stakeholders, and undertaking other efforts that would advance the state of knowledge on the intersection of AI/ML and discrimination. The regulators should prioritize research that analyzes the efficacy of specific uses of AI in financial services and the impact of AI in financial services for consumers of color and other protected groups.
G. Hire staff with AI and fair lending expertise, ensure diverse teams, and require fair lending training
AI systems are extremely complex, ever-evolving, and increasingly at the center of high-stakes decisions that can impact people and communities of color and other protected groups. The regulators should hire staff with specialized skills and backgrounds in algorithmic systems and fair lending to support rulemaking, supervision, and enforcement efforts that involve lenders who use AI/ML. The use of AI/ML will only continue to increase. Hiring staff with the right skills and experience is necessary now and for the future.
In addition, the regulators should also ensure that regulatory as well as industry staff working on AI issues reflect the diversity of the nation, including diversity based on race, national origin, and gender. Increasing the diversity of the regulatory and industry staff engaged in AI issues will lead to better outcomes for consumers. Research has shown that diverse teams are more innovative and productive36 and that companies with more diversity are more profitable.37 Moreover, people with diverse backgrounds and experiences bring unique and important perspectives to understanding how data impacts different segments of the market.38 In several instances, it has been people of color who were able to identify potentially discriminatory AI systems.39
Finally, the regulators should ensure that all stakeholders involved in AI/ML—including regulators, financial institutions, and tech companies—receive regular training on fair lending and racial equity principles. Trained professionals are better able to identify and recognize issues that may raise red flags. They are also better able to design AI systems that generate non-discriminatory and equitable outcomes. The more stakeholders in the field who are educated about fair lending and equity issues, the more likely that AI tools will expand opportunities for all consumers. Given the ever-evolving nature of AI, the training should be updated and provided on a periodic basis.
III. Conclusion
Although the use of AI in consumer financial services holds great promise, there are also significant risks, including the risk that AI has the potential to perpetuate, amplify, and accelerate historical patterns of discrimination. However, this risk is surmountable. We hope that the policy recommendations described above can provide a roadmap that the federal financial regulators can use to ensure that innovations in AI/ML serve to promote equitable outcomes and uplift the whole of the national financial services market.

Kareem Saleh and John Merrill are CEO and CTO, respectively, of FairPlay, a company that provides tools to assess fair lending compliance and paid advisory services to the National Fair Housing Alliance. Other than the aforementioned, the authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. Other than the aforementioned, they are currently not an officer, director, or board member of any organization with an interest in this article.

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China’s payments u-turn: Government over technology

China has been at the forefront of a technological revolution in payments in both its private and public sectors. China’s tech firms succeeded in replacing the bank-based magnetic striped card world with a tech-based QR code system. Then the People’s Bank of China (PBOC) launched its central bank digital currency, followed by a series of government actions that appear designed to steer the Chinese system away from these tech firms. What is going on in Chinese payments is a fascinating battle of private sector innovation versus government control and big-tech versus big-banks, putting the usually staid and boring world of payment systems into the spotlight allowing for examination of broader narratives about the future of China and how it is playing the global economic game. It also offers insight into how the Federal Reserve plans to approach digital payments in America.

Aaron Klein

Senior Fellow – Economic Studies


The Chinese payment wars stand in sharp contrast to the standard analysis of the global economic game. In the standard model, the United States is the advanced incumbent economy while China is playing economic catch-up. China is simultaneously modernizing its own domestic system to resemble western economies while at various levels integrating into the broader global financial system. The story in payments begins along this common narrative. The U.S. created and essentially dominates global retail payments through a magnetic stripe card-based interface running through the global banking system. This system has its roots in a series of inventions from roughly 50 years ago in New York, which began as a set of solutions for restaurants and frequent customers who were unable to access cash over the weekend and sought an alternative to the paper-based check payment system. These ‘Diners Cards’ eventually transformed into a series of plastic cards, building a set of payment rails that process more than 130 billion transactions a year in the United States, which is more than 350 million transactions per day. To put that in perspective, the peak number of daily transactions in Bitcoin is estimated around 400,000.
Magnetic striped cards came to dominate the world of retail payments in developed economies. At an earlier point in its economic development, China attempted to emulate and graft onto this system, with multiple banks introducing their own sets of magnetic stripes and cards including Union Pay as the most prominent example. Founded in 2002, Union Pay’s prevalence rose sharply to achieve over 3.5 billion cards in circulation in just a decade and volume that was roughly half of what Visa was processing in the mid 2010s.
The story diverges with Chinese technology companies, WeChat and Alibaba, who appreciated the inherent inefficiencies in the card-based system: the interchange fees, design apparatus of cards and card readers, and the costs borne by merchants. Chinese merchants, particularly small ones, lacked interest in such a costly system. Exploiting these opportunities, the two tech firms created a QR code digital wallet scan-based system, which essentially leapfrogged the debit magnetic cards. The new system was faster and more efficient than debit magnetic cards, producing a host of direct and indirect benefits for those two companies as well as for broader society. This innovation allowed China to leapfrog the magnetic striped card system that dominates much of the western world’s retail payment system.
China’s new payment system exploded from inception to dominance in under a decade. With over a billion users on each platform, the power of network incentives has been unleashed. The new payment system has replaced cards and cash at registers, changed how families give gifts, and even evolved the way how beggars ask for money, with QR codes replacing tin cups.
This is a powerful example of Chinese innovation, competition, and adoption. It appears, at least to outside observers, to be highly organic and internally driven, not a product of central planning or committees. For example, the two companies diverged in the origin of their payment systems. WeChat Pay is based on a social media platform (for Americans think Facebook) and is heavily engaged in person-to-person payments. WeChat Pay first rolled out as a service to facilitate personal funds in the form of ‘Red Envelopes’ (traditional gifts of cash) around the Lunar New Year in 2014. WeChat Pay proposed digitizing this exchange, which given their person-to-person social media network, was clearly synergistic. The popularity of Red Envelope exchanges seeded many customers’ WeChat Pay accounts with initial funds. WeChat launched the Red Packet digital payment idea in 2014, and 16 million packets were sent. The next year, 1 billion packets were sent. By 2016, it was over 8 billion and in 2017, 46 billion.

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Alipay’s origin differs. Alipay is a payment platform developed by Chinese tech conglomerate Alibaba with roots in digital commerce (think Amazon) and hence more likely to be used for business purposes. Internet commerce requires electronic payment systems, which were integrated with credit and debit cards. The lack of such a system in China incentivized Alibaba to develop Alipay to support its Taobao online shopping platform. With Alipay’s main competitor, UnionPay, having only recently launched and not having gained many customers, the payment market was wide open. Alibaba offers incentives for merchants to use Alipay for purchases throughout their platform. They offer feeless purchases for both parties, preferential placement on digital platforms for merchants, and the ease of payment integration into business processing. Those differences provide economic benefits of lower costs and potentially greater transaction volumes that are not widely available in the bifurcated credit/debit card system.
There are potential drawbacks to this integrated model, including the lack of fees to provide services customers want with payments – such as interest-free grace periods of credit – and anti-competitive concerns of integrating business platforms and social networks with payment platforms.
With this technological advance, China had many of the ingredients necessary to challenge the existing retail payments system and seemed poised to leap into the global payments contest, which is in desperate need of an advance from the 50-year-old plastic cards that seem woefully out of place in the digital environment.
However, it appears that China has not chosen to do this, instead making a u-turn and now heading in the other direction. Rather than aggressively expanding the system and opening it to a broader network in the way that the American card-based system did, China has taken a series of measures to slow the tech companies, enhance the government’s role, and possibly bring payments back into a bank-centric system.
China’s government intervened with the creation of a central bank digital currency. This digital yuan uses much of the same infrastructure as the Ali and WeChat pay systems: digital wallets, QR codes, scanners, etc. Just this month PBOC Governor Yi Gang stated a goal of “interoperability with existing payment tools” for the digital yuan.
The digital yuan is currently running in more than 10 regions of China with more than 150 million users. It was first launched in Shenzhen, the home city of Tencent (the company that runs WeChat Pay). It does not take a skilled U.S.-China international diplomat with a keen understanding of history to understand that deciding to roll out the digital yuan in the hometown of the payment giant sends a clear message. If the U.S. government started its own online bookstore/retailer and happened to choose the city of Seattle, the message would be globally clear.
Couple this with Alibaba’s aborted initial public offering of its financial arm Ant and the sweeping set of problems cited by government officials and regulators and there is a message that China is pausing any potential for global expansion of the Alipay and WeChat payment systems. To the contrary, what seems to be happening is that rather than exporting Chinese-based digital wallets in hopes of becoming as ubiquitous as the Visa, MasterCard and American Express networks are currently, there is instead a desire to reorient the internal Chinese system to be focused on a central bank digital currency run through digital wallets more directly tied to the Chinese banking system.
Now, it is plausible that this change ultimately sets up a digital yuan using very similar technological rails of QR codes, first piloted by Ali and WeChat that would in fact be analogous to history repeating. The original American charge card, Diners Club, coordinated between restaurants (merchants) and consumers, not banks. This model ultimately lost the race. MasterCard is itself a consortium of financial institutions with a very different history than Visa, which was born from Bank of America, and American Express which began as a closed loop payment system and today is part of a bank holding company.
Previously, it seemed plausible that a digital wallet from Alipay or linked to the WeChat network could be a global phenomenon spreading far beyond China in the phones and pockets of billions of people worldwide. That now feels very unlikely. Instead, digital Chinese wallets through Chinese banks appear where China is headed. That model seems an unlikely mode to facilitate international commerce throughout Europe, or even Africa, let alone to challenge the United States for domestic market share. Though Alipay and WeChat are accepted in the United States in retail stores, they are almost exclusively used by Chinese individuals, not by Americans.
This begs the question: when China does make technological advances in globally competitive industries such as payments, is China’s ultimate goal to export this technology and create a network for global commerce? Or is it ultimately an internal process where the benefits and costs will be felt by Chinese nationals and control will be maintained by the Chinese government? Gunpowder was invited in China centuries before the formula came to Europe who used it very differently.
From an American perspective, there’s a bit of a sigh of a relief because China had built a better mousetrap in many respects. It is also a shot in the arm for the Federal Reserve, which has devoted significant resources to considering launching its own central bank digital currency. China was not the only entity pushing the Federal Reserve. Facebook’s original announcement of launching a digital currency (then called Libra, now known as Diem) was another key moment energizing the Fed to consider alternatives. The Fed’s consideration of a central bank digital currency has been heavily impacted by the payments actions proposed by both China and Facebook. This helps explain how the same Federal Reserve that failed to adopt a real-time payment in the U.S. despite the European Union, United Kingdom, Japan, Mexico, and many more countries adopting such a system years and decades earlier is now devoting significant attention to creating a new central bank digital currency. Whether the Fed launches a new digital currency or not, is years away. In the meantime, low income consumers still pay billions as a result of the Fed’s failure to modernize its payment system. By my estimate more than $100 billion has already been taken as a result of the Fed’s failure to act when the United Kingdom transitioned more than a decade ago. It marks one the largest failures of policy that contributes to income inequality and needless inequity in America in my lifetime.
In conclusion, whereas it is currently unclear whether the Federal Reserve will launch a central bank digital currency, it appears that China is committed to a path of a digital yuan. It seems likely that such a move will also favor moving payments more broadly back into its banking system, away from its two technological companies. However, the technological system of QR codes and digital wallets appears likely to remain in China regardless of who operates the system.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.

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Credit scoring: Improve or eliminate?

Your credit score plays a major role in your life, impacting your ability to rent an apartment, buy a house, get a credit card, and even how much you pay for auto insurance. These three-digit numbers, graded on a scale that resembles the SAT, have become more accessible to consumers due to recent changes in law, technology, and business. Credit scores are clearly impactful in the lives of Americans, but are they being created accurately, fairly, and with proper regulatory oversight? Is there a better way?
Credit scores are built on credit reports, files kept on most Americans by several large credit reporting bureaus. What are these reports and scores made of? How accurate are they? Who ensures they are fair and accurate?
On December 7, the Center on Regulation and Markets will convene a group of experts to discuss these questions and get to the core of the issue: Are credit scores and credit reports the right method for society to allocate credit? If so, how can they be improved? If not, what should replace them?
Viewers can submit questions for speakers by emailing [email protected] or via Twitter using #CreditScore.

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How Fintech Companies Can Mitigate the Racial Wealth Gap

On November 2, 2021, Brookings Metro Fellow Kristen Broady testified to the U.S. House Financial Services Committee’s Task Force on Financial Technology, during a hearing titled Buy Now, Pay More Later? Investigating Risks and Benefits of BNPL and Other Emerging Fintech Cash Flow Products.

Broady’s research, as detailed in her written testimony, shows how fintech companies can mitigate racial financial health and wealth gaps that hamper Black and Hispanic families’ financial security through product offerings and policies they put in place. Through technology and automation, they can reduce costs and prices, speed up delivery and increase convenience for underserved populations (Saunders, 2019). Over the past 20 years, fintech companies have provided new ways to capture data, reach broader audiences, and expand access to credit (Strochak, 2017). These companies also have the potential to think differently about policies and programming that can amplify opportunities for Black and minority communities. These private sector innovations can be paired with public policy interventions as well as to address some of the systemic issues that have contributed to the financial health and wealth gaps.
Broady provides several steps that public policymakers can take to increase financial health, including:

Increase investments in the CDFI Fund and make any relevant programs that sunset (like NMTC) permanent.
Create a mandatory financial health curriculum for middle and high schoolers.
Enhance broadband deployment.
Raise minimum wage for companies with over 500 employees.
Foster utilization of the CFPB Special Purpose Credit Program (SPCP).
Revise and revive the SBIC program under the SBA to incentivize private sector investments in BIPOC founders.
Revise SBA 7(a) program to enable fintechs to more easily engage with the program.

The ongoing COVID-19 pandemic has disproportionately impacted the Black community in terms of health and economic effects and shined a light on historical racial wealth and financial health gaps in America. Closing these gaps will require that structural, systemic, and historical economic disparities are addressed through significant public policy changes.
To read Broady’s full testimony, click here. To watch the testimony video, click here.

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Expensive and growing: Loudoun County, Va.


November 4, 2021

Loudoun County is rapidly growing, high-priced county located in a growing, high-priced metropolitan area (Washington, D.C.). All but one of the metro area’s 25 jurisdictions saw positive population growth from 2009 to 2019, and eight jurisdictions fall into the highest cost category (housing value-to-income ratios over 4). Loudoun had the second highest population growth rate in the metro area (0.25).

To develop a more complete picture of housing market conditions in Loudoun County, we draw on a broader set of measures that capture demand, affordability of both owner-occupied and rental housing, and housing quality (Table 1). 

Key findings from this comparison are:

Loudoun County’s population growth rate, 0.35, is more than double that of the average county in the D.C. metro area, and well above the national average. Fast population growth drives the demand for additional housing.The typical household in the Washington D.C. metro area would have to pay 4.9 times their annual income to purchase the median home in Loudoun County. Home value-to-income ratios between 2.5-3.5 are considered healthy.Households earning less than $74,800 (or 78% of the metro area median income) would have difficulty paying rent for the median rental home in Loudoun County, while spending no more than 30% of their income on rent. While most middle-income households in the metro area can afford median rent in Loudoun, most low- and moderate-income households in the region will fall below this threshold.20.2% of renters in Loudoun County are severely cost burdened, meaning they spend more than half their income on rent. That is slightly below the severely cost-burdened share for the entire D.C. metro area and below the national average.The vacancy rate, 3.3%, is very low. Vacancy rates of 6-10% are considered healthy. Low vacancy rates are an indication that supply is not keeping up with demand.The housing stock is quite new relative to the region and country: only 2.8% of homes were built prior to 1940, while 72.6% were built after 1990. New housing is generally higher quality than older homes and more expensive to buy or rent, while having lower maintenance costs.

Recommended policy solutions

Increase housing supply. Housing is expensive because supply has not kept up with demand. High prices and rents, combined with low vacancy rates, indicate that there is unmet demand for housing. It is important to realize that no single county can produce enough housing to meet demand for the entire metro area. Reducing housing costs in expensive, supply-constrained metro areas will require sustained periods of increased housing production across multiple jurisdictions. All high-cost counties within a metro area adopting the strategies described below will have better results than actions by a single county, and county officials can play a leading role in coordinating across jurisdictions and sectors to achieve those goals.

About the Authors

Jenny Schuetz

Senior Fellow – Metropolitan Policy Program, Future of the Middle Class Initiative

Tim Shaw

Associate Director of Policy – Aspen Institute

Make it easier to build small, moderately-priced homes. In expensive metro areas, the size of homes and the amount of land used per home are major factors in the price of individual homes. Single-family detached homes on large lots are the most expensive structure type. Rowhouses, townhomes, two-to-four family homes, and low-rise apartment buildings have lower per-unit development costs than detached homes. These structures are also well suited for rental housing, which is more affordable to moderate-income households, and as “starter homes” for prospective first-time buyers who are currently priced out of the market. Zoning changes that enable smaller, less land-intensive structures to be built as-of-right in more parts of the county will increase the diversity of housing choices and widen the price range of available homes. Companion zoning reforms include relaxing dimensional requirements, such as minimum lot sizes, setbacks, lot coverage, or floor-to-area ratios. Reducing minimum parking requirements and allowing flexibility in design standards can also result in cost savings for newly built homes.

Developing a specific menu of zoning reforms will require an assessment of the county’s current housing types, density, and land availability. Exactly what types of zoning reform will yield the largest supply increases and cost reductions will vary across high-cost counties. A locality that has predominantly detached homes on one-acre lots—typical of many outer suburbs—could realize substantial cost savings by allowing rowhouses on 4,000 square foot lots. For urban counties and inner-ring suburbs that currently have many small-lot homes and little undeveloped land, increasing housing supply may require zoning reforms that allow redeveloping single-family homes, parking lots, or commercial buildings as low- or mid-rise apartments. High-rise construction has the highest per-square-foot costs, and will typically only occur in places with very expensive land and high rents.

Make the development process simpler and shorter. The length of time required to complete development projects, combined with the complexity of the process, are significant factors in the price of newly built housing. Local development processes that make decisions on a case-by-case basis, rather than following consistent, transparent rules, increase the uncertainty and risk of development, which translates into higher costs. Discretionary processes include requiring special permits (also called conditional use permits), site plan reviews, environmental impact reviews, and negotiations over impact fees all add to development costs. Policy changes that reduce development time and complexity include allowing more development as-of-right; integrating approvals for multiple parts of the development process through a one-stop-shopping approach; setting a clear and transparent impact fee schedule; and setting deadlines that require decisions to be made within a set period of time.

Expand vouchers or income supports for low-income renters. Even in communities where enough housing is built to accommodate increased demand, market-rate housing remains unaffordable to many low-income households. The poorest 20% of households everywhere in the U.S. spend more than half their income on housing, well above the threshold HUD defines as affordable. Only one in four eligible households receives federal rental assistance, including vouchers and public housing. Local governments that have sufficient resources can supplement these programs through locally funded rental vouchers or direct income supports. These programs require an ongoing funding source; high-income counties may be able to finance local vouchers from general tax revenues such as property or sales taxes, while lower-income counties will require support from state or federal governments. 

An alternative to household-based subsidies for low-income households is to provide land or financial support for acquisition or construction of affordable housing. Local jurisdictions often own or have significant control over physical assets—such as publicly owned land or airspace—that can be leveraged to increase the availability of affordable housing in the community. Affordable housing trust funds are a flexible financing vehicle to support these activities.

Housing market conditions can vary across submarkets within counties. These policy recommendations are based on an assessment of overall county-level housing metrics. Larger counties often have multiple distinct submarkets with varying affordability, physical quality, infrastructure availability, and development regulations. Cities, towns, and neighborhoods that offer the best economic opportunity—proximity to well-paid jobs, transportation, good schools, and other amenities—often have housing that is too expensive for moderate-income households in the county. Lower-cost communities tend to have older, poorer quality housing. Addressing within-county disparities in housing costs, availability, and quality may require coordinating between independent political entities (e.g., separate cities and towns) in counties with more fragmented local government.

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Focus on bank supervision, not just bank regulation

Last month, the Biden administration made headlines nominating Cornell Law School Professor Saule Omarova to serve as Comptroller of the Currency, a position from which she would oversee the National Banking System. Omarova’s nomination has drawn sharp criticism from the financial services industry, placing her alongside other Biden appointments within financial regulation such as chair of the Securities and Exchange Commission Gary Gensler and director of the Consumer Financial Protection Bureau (CFPB) Rohit Chopra. In each case, the appointments represent a sea change, embracing an approach to regulation that starkly differs from the priorities of the Trump administration. These existing appointments and nominations set the stage for the financial regulatory appointments that the administration has not yet made, including three vacancies on the Federal Reserve — the Fed Chair, Vice Chair for Supervision, and a member of Fed’s Board of Governors —and vacancies at the Federal Deposit Insurance Corp (FDIC), among others.

Debate over these key personnel focus on different visions of regulation, the rules political appointees write that apply to the entire financial system. These include what financial regulation should do about climate change, how it should support under-represented minorities, how it can ensure financial stability, and much more. But the biggest piece of the puzzle is still missing: these agencies and appointments also control the supervision of the financial system, not just its regulation. The difference between these two concepts is very important. If regulation sets the rules of the road, supervision is the process that ensures obedience to these rules (and sometimes to norms that exist outside these rules entirely). Regulation is the highly choreographed process of generating public engagement in the creation of rules. Supervision is the mostly secret process of managing the public and private responsibilities over the risks that the financial system generates.
Political groups organize in support or opposition of various regulatory nominees usually on the basis of the candidates’ perceived regulatory priorities. This is important, but this exclusive focus is a mistake. We, all of us, should pay far more attention to the candidates’ vision and philosophy of supervision. This part of the public vetting is all the more important given the culture of secrecy that surrounds bank supervision. If the public is going to have a say in the kind of supervisory system we should have, then the appointment process is likely the first and last chance to do it.
The question for senators who provide advice and consent necessary to obtain these jobs and for the general public in vetting these candidates for appointment should focus on how these nominees view the tradeoffs inherent in the supervisory process wholly independent of financial regulation. It should focus on what they will do—now—to maintain the culture of supervision or to change it.
What Supervision Means
The idea that supervision and regulation should receive separate priorities is not new. More recently, in contemporary debates, some view supervision as part of a long-standing settlement of monetary questions of special relevance during the Civil War, others as the implementation of regulatory priorities, others as a kind of regulatory monitoring. We view it differently. While many of these conceptions of supervision capture elements of what makes this mode of public governance so unusual, the full picture is more historically contingent and flexibly comprehensive. In our view, supervision is the management of residual risk at the boundary of public and private, the space where private banks and public officials sometimes spar, sometimes collaborate, over responsibility over the financial system. Because risks in the financial system are constantly evolving,  supervision has done the same.
In our work on the history of bank supervision, we offer a typology that captures this range of functions. The typology has two parts. First, supervision functions as a distinct mechanism of legal obedience—a means by which government or private actors seek to alter bank behavior. These mechanisms can be displayed on two axes, between public and private mechanisms, which require the exercise of coercive and non-coercive power.

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In this sense, supervision represents a choice for policymakers, distinct from other alternatives. It is a choice, as the graphic indicates, which authorizes government officials to exercise substantial discretion about how to alter behavior.

Relatedly, what supervision looks like on the ground depends almost entirely on what supervisors think they are trying to accomplish, self-conceptions that divide not only according to an external logic of coercion vs. non-coercion and public vs. private but also an internal one. These self-conceptions operate within a framework with axes spanning punitive to collaborative and from retrospective to prospective, as summarized in Figure 2.

Together, the two typologies indicate a range of possible supervisory actions (external) and the motivations behind such actions (internal). They imply constant trade-offs that supervisors must make as they share risk management responsibilities with participants in the private sector. Despite the extraordinary flexibility that this model of supervision permits, supervisors cannot hold all ground at once and be all things to all people. They must choose and in choosing navigate the often conflicting and sometimes contradictory policy goals placed by Congress on bank supervisors: between safety and soundness and firm competitiveness, between consumer protection and facilitation of financial innovation, between punitive and collaborative approaches, and many others.
When a new public official is appointed to lead one of the major elements of bank supervision, she inherits a toolbox with many different kinds of tools. Members of Congress should ask nominees which tools they prefer for which kinds of jobs, how they view these trade-offs and what they would prioritize, and how they think about alternatives. A short-hand method is to listen for the metaphors nominees use to describe supervision. Do nominees conceive of supervisors as cops on the beat? As fire wardens? As referees and umpires? As compliance officers? As management consultants? Are banks their customers? What tools will they use in accomplishing this vision? What flexibility will they use and under what circumstances?
Who Supervisors Are
Because supervision is fundamentally flexible and evolving, personnel decisions are vital. Supervisory officials—independent of legislative and regulatory processes—constantly reshape the methods, tools, and rationales of supervision in relation to their understandings of financial risk and their evaluation of relevant policy tradeoffs.
History provides rich examples of this process. Looking back to the nineteenth century, comptrollers, and later officials at the Federal Reserve and FDIC, created supervisory bureaucracies with little congressional guidance on how those bureaucracies should be structured. In doing so, they crafted supervisory tools, like standardized examination forms or “schools” of supervision complete with simulated banks getting practice exams, which guided frontline agency staff and bankers through the thicket of managing residual financial risk. Sometimes appointees proved too lenient or too eager to encourage bank chartering and growth at the expense of systemic safety. At other times they proved too harsh, making a theatrical display of cracking down on shoddy bank oversight and in doing so potentially undermining agency credibility with bankers who doubt supervisors’ intentions.
Two recent examples highlight the ways conceptions of supervisory purpose translate into agency action. First, the CFBP emerged in the wake of the 2008 financial crisis in part because existing supervisory agencies tended to sacrifice consumer protection. In its early years, the architects of the CFPB adopted Senator Elizabeth Warren’s “cop on the beat” approach, bringing enforcement lawyers to routine exams even when there was no enforcement action pending. Thus, while other agencies tended to have a collaborative and prospective view of consumer protection—identifying potential problems and helping bankers navigate past them—the CFPB was looking to punish past mistakes and ensure compliance in the future. Bankers struggled to reconcile the agency’s seemingly contradictory positions. The enforcement attorneys left the examination teams, but the tone set from the top continued to be decisively important. Under Democrat Richard Cordray, the CFPB leveled more than $5.5 million in fines a day compared to slightly less than $2 million per day under Trump appointee Kathy Kraninger. The CFPB used civil enforcement aggressively and then didn’t.
Second, the design and implementation of stress tests for the nation’s largest, most systemically important banks has also undergone significant change at the hands of Federal Reserve appointees Dan Tarullo and Randy Quarles. There is much that is purely regulatory about these changes—the pace of stress tests, the reliance on qualitative versus quantitative metrics. But stress tests are ultimately a supervisory activity leaving a huge amount of space for supervisors to shape individual responses to idiosyncratic factors. The question for a new head of an agency is not simply what regulatory rules will govern stress tests but how that official thinks supervisory interactions with individual banks through the stress test process should occur.
Finally, changes in the approach to and methods of supervision seldom spring fully-formed from the head of a Senate-confirmed nominee. Rather, supervisory officials must also develop plans for recruiting, training, and retaining a superb corps of supervisors who can be independent of banks but also expert at managing those relationships to alter bank behavior. In doing so, they face two challenges. First, political appointees necessarily take the helm of an unwieldy ship. Frontline supervisors are in place and working hard in offices across the country. They have methods, routines, and ingrained expectations developed over years of experience and training by distant political appointees whose visions and ideologies of supervision may have been repudiated by a recent election. Supervisory leaders must have a strategy for learning their agencies’ bearing and changing course as necessary. They must do so, secondly, while holding onto experienced staff who may be attracted to the lure of more lucrative private-sector jobs. Retaining and retraining staff is a signature challenge of bank supervision. Senators should inquire about nominees’ plans for doing so.
Ten questions policymakers should ask
If Biden administration officials and senators heed our calls to take supervision seriously, then there are a number of questions they should direct to candidates and nominees alike.

What do you see as the purpose of supervision? What is the supervisory agency’s primary role? What, if any, secondary goals would you emphasize?
How would you plan to balance the inherent trade-offs between these goals? How would this balance differ—if at all—from your predecessor?
Which supervisory tools do you view as most important for accomplishing these goals? How would you use these tools differently than your predecessors?
How do you plan to learn about the existing supervisory culture at your agency? How do you plan to realign that culture around your goals?
How will you recruit, train, and retain supervisory staff?
How will you organize your office such that regulatory and supervisory functions inform each other but do not absorb each other?
How do you plan to work with other bank supervisory agencies—at the federal level, internationally, and in the states? Do you see these other agencies as competitors or collaborators?
What is the relationship between supervision and enforcement in your agency? How will you manage the process through which supervisors learn sensitive information that may be relevant to an enforcement action but that may also be an opportunity to change bank behavior without enforcement?
How will you ensure that bank supervisors do not unduly adopt the point of view of the banks supervised?
How will you ensure that bank supervisors understand the point of view of the banks supervised?

Although regulatory issues such as climate change, federal bank chartering, diversity, and fintech dominate conversations about regulatory appointments, a lack of focus on supervisory issues comes at a great cost to public governance and financial stability. Fights over regulation that ignore supervision may obscure these critical issues more than they illuminate. Bank supervision is an unusual set of institutions, homegrown in the United States and refined by federalism, financial crisis, and historical accident. Supervision remains the most important tool in the federal administrative toolkit for changing the way we understand the business of banking. The process of public governance should give it its due.

The Brookings Institution is financed through the support of a diverse array of foundations, corporations, governments, individuals, as well as an endowment. A list of donors can be found in our annual reports published online here. The findings, interpretations, and conclusions in this report are solely those of its author(s) and are not influenced by any donation.
Sean Vanatta is a Lecturer in U.S. Economic and Social History at the University of Glasgow, and an un-paid member of the Federal Reserve Archival System for Economic Research (FRASER) Advisory Board. The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. Other than the aforementioned, they are currently not an officer, director, or board member of any organization with an interest in this article.

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Greening Asia for the long haul: What can central banks do?

Here in Hong Kong, Category 8 typhoons used to be infrequent. Just as floods in Germany and massive wildfires in California were disasters we might see every decade or so. Sadly, this is no longer the case. In one week alone this October, Hong Kong saw two Category 8 typhoons. Earlier this year, floods devastated parts of central Germany, and California saw five of the largest wildfires in its history in 2020. Hong Kong, Germany, and California are not outliers. Extreme weather conditions have been documented in much of the world.

It is widely recognized that climate change implies more frequent and severe weather events, greatly increasing the physical risks to financial and economic stability. In the absence of urgent action, the impact will be widespread and affect most countries, people’s lives and livelihoods, and many industries. The financial sector will be no exception.
Central banks have an important role to play in responding to this challenge. They are interested in these issues for at least three reasons: (i) preserving financial stability as societies move toward reducing their carbon footprints; (ii) diversifying the investments of central bank reserves to minimize unnecessary risks; and (iii) in keeping with their mandates and expertise, providing support to global efforts to achieve the objectives of the Paris climate accord.
The Asia-Pacific region has the largest need for infrastructure investments, around $1.7 trillion per year for developing Asia alone, according to the Asian Development Bank (ADB). Much of this needs to be green investment. It is also among the most vulnerable regions if actions to combat climate change are not taken urgently. At the same time, Asia has, relatively speaking, an extremely large pool of foreign exchange reserves, at around $5.7 trillion by the end of 2019. So, there is a clear case that we need to find a way to bring these two together and do so creatively and safely. The BIS Asian Green Bond Fund is an attempt in this direction.
The fund is designed to provide central banks with opportunities to invest in high quality bonds issued by sovereigns, supranationals, and corporations that comply with strict international green standards. It has two distinct features. First—compared to the BIS’ previous green bond funds—it has a broader group of eligible issuers. It will invest in corporations, including financial firms, because much of the financing in Asia is through commercial banks rather than directly from capital markets. Second, the BIS has engaged with multiple international financial institutions and development finance institutions—the ADB, the Asian Infrastructure Investment Bank, the World Bank, and the International Finance Corporation—to explore opportunities for collaboration to develop a pipeline of green products in the region to invest in. These institutions also have expertise on the ground to ensure that the highest standards are being followed in forming these green investment opportunities to allay concerns about greenwashing.
On the technical side, in line with reserve managers’ appetite for safety, liquidity, and return, all central features of the BIS’s financial products, the Asian Green Bond Fund would be established as a BIS Investment Pool (BISIP), a collective investment scheme structured under Swiss law that is commonly used by the BIS for its fixed income investment products.

While the fund would provide the opportunity for central banks to invest their reserves into greening the economies in the region, the fund is not restricted to investors from Asia. Broad interest to invest in the fund has been expressed by central banks well beyond Asia, reflecting the need of the global central banking community for reserve diversification. Regardless of the domicile of the central bank, central bank investments are generally made with a long-term investment horizon in mind. The fund will thus help channel global central bank reserves to green projects for long-term sustainable growth in the region, which has contributed more than two-thirds of global growth in the past decade.
In light of the fast-changing developments of the green bond market in the Asia-Pacific region, the Asian Green Bond Fund will be an evolving fund, allowing it to be agile and make changes as needed. For example, while the fund will be denominated in U.S. dollars initially, green bonds denominated in Asian local currency will be considered at its first and/or second anniversary. Similarly, while ICMA (International Capital Market Association) and CBI (Climate Bond Initiative) standards will be used at the start, the BIS is open to alternative standards, for example, for bonds funding projects that may not be green at present but are aligned with a transition toward low-carbon activities.
Technologists often say: “Think big, start small and scale fast.” That is how the BIS too plans to approach this endeavor. The Asian Green Bond Fund will represent an important addition to the existing suite of green bond funds at the BIS. As the fund evolves, it will also allow central banks to consider ways of expanding green financial markets, either through diversifying into regional currencies, or by considering the next generation of sustainable bonds adhering to even stricter standards and aligned to the objectives of the Paris accord.

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Greening Asia for the long haul: What can central banks do?

Here in Hong Kong, Category 8 typhoons used to be infrequent. Just as floods in Germany and massive wildfires in California were disasters we might see every decade or so. Sadly, this is no longer the case. In one week alone this October, Hong Kong saw two Category 8 typhoons. Earlier this year, floods devastated parts of central Germany, and California saw five of the largest wildfires in its history in 2020. Hong Kong, Germany, and California are not outliers. Extreme weather conditions have been documented in much of the world.

It is widely recognized that climate change implies more frequent and severe weather events, greatly increasing the physical risks to financial and economic stability. In the absence of urgent action, the impact will be widespread and affect most countries, people’s lives and livelihoods, and many industries. The financial sector will be no exception.
Central banks have an important role to play in responding to this challenge. They are interested in these issues for at least three reasons: (i) preserving financial stability as societies move toward reducing their carbon footprints; (ii) diversifying the investments of central bank reserves to minimize unnecessary risks; and (iii) in keeping with their mandates and expertise, providing support to global efforts to achieve the objectives of the Paris climate accord.
The Asia-Pacific region has the largest need for infrastructure investments, around $1.7 trillion per year for developing Asia alone, according to the Asian Development Bank (ADB). Much of this needs to be green investment. It is also among the most vulnerable regions if actions to combat climate change are not taken urgently. At the same time, Asia has, relatively speaking, an extremely large pool of foreign exchange reserves, at around $5.7 trillion by the end of 2019. So, there is a clear case that we need to find a way to bring these two together and do so creatively and safely. The BIS Asian Green Bond Fund is an attempt in this direction.
The fund is designed to provide central banks with opportunities to invest in high quality bonds issued by sovereigns, supranationals, and corporations that comply with strict international green standards. It has two distinct features. First—compared to the BIS’ previous green bond funds—it has a broader group of eligible issuers. It will invest in corporations, including financial firms, because much of the financing in Asia is through commercial banks rather than directly from capital markets. Second, the BIS has engaged with multiple international financial institutions and development finance institutions—the ADB, the Asian Infrastructure Investment Bank, the World Bank, and the International Finance Corporation—to explore opportunities for collaboration to develop a pipeline of green products in the region to invest in. These institutions also have expertise on the ground to ensure that the highest standards are being followed in forming these green investment opportunities to allay concerns about greenwashing.
On the technical side, in line with reserve managers’ appetite for safety, liquidity, and return, all central features of the BIS’s financial products, the Asian Green Bond Fund would be established as a BIS Investment Pool (BISIP), a collective investment scheme structured under Swiss law that is commonly used by the BIS for its fixed income investment products.

While the fund would provide the opportunity for central banks to invest their reserves into greening the economies in the region, the fund is not restricted to investors from Asia. Broad interest to invest in the fund has been expressed by central banks well beyond Asia, reflecting the need of the global central banking community for reserve diversification. Regardless of the domicile of the central bank, central bank investments are generally made with a long-term investment horizon in mind. The fund will thus help channel global central bank reserves to green projects for long-term sustainable growth in the region, which has contributed more than two-thirds of global growth in the past decade.
In light of the fast-changing developments of the green bond market in the Asia-Pacific region, the Asian Green Bond Fund will be an evolving fund, allowing it to be agile and make changes as needed. For example, while the fund will be denominated in U.S. dollars initially, green bonds denominated in Asian local currency will be considered at its first and/or second anniversary. Similarly, while ICMA (International Capital Market Association) and CBI (Climate Bond Initiative) standards will be used at the start, the BIS is open to alternative standards, for example, for bonds funding projects that may not be green at present but are aligned with a transition toward low-carbon activities.
Technologists often say: “Think big, start small and scale fast.” That is how the BIS too plans to approach this endeavor. The Asian Green Bond Fund will represent an important addition to the existing suite of green bond funds at the BIS. As the fund evolves, it will also allow central banks to consider ways of expanding green financial markets, either through diversifying into regional currencies, or by considering the next generation of sustainable bonds adhering to even stricter standards and aligned to the objectives of the Paris accord.

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