September 24, 2021

Bruegel Publications

Brexit and European finance: Prolonged limbo

BY EU-UK relations, Finance & Financial Regulation, Financial Regulation, Bruegel

Share on twitter
Share on whatsapp
Share on facebook
Share on linkedin
Share on email
Share on reddit

Listen to our reports with a personalized podcasts through your Amazon Alexa or Apple devices audio translated into several languages

Bruegel Institute

Originally published on by Bruegel Institute. Link to original report

( < 1 mins)

Read the full article published in CESifo Forum.

Decarbonisation of the energy system

( 3 mins) Our analysis highlights that the current national energy and climate plans (NECPs) of EU countries are insufficient to achieve a cost-efficient pathway to EU-wide climate neutrality by 2050.

Read More »

Who should regulate: Chairs or majorities of the board

( 12 mins) 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

Twitter
AaronDKlein

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.
Background
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

Agency
OCC, CFPB, FHFA, OFR
NCUA

FDIC
SEC
CFTC
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.

Related Content

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

Aaron Klein
Wednesday, December 15, 2021

Financial Regulation
Focus on bank supervision, not just bank regulation

Peter Conti-Brown and Sean Vanatta
Tuesday, November 2, 2021

Financial Regulation
Where is the Fed Vice Chair for Supervision?

Peter Conti-Brown
Tuesday, October 26, 2021

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.

Read More »

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

( 8 mins) 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

Twitter
AaronDKlein

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

Related Content

Financial Regulation
An AI fair lending policy agenda for the federal financial regulators

Michael Akinwumi, John Merrill, Lisa Rice, Kareem Saleh, and Maureen Yap
Thursday, December 2, 2021

Financial Regulation
Focus on bank supervision, not just bank regulation

Peter Conti-Brown and Sean Vanatta
Tuesday, November 2, 2021

Financial Institutions
Overdraft fees are big money for small banks

Aaron Klein
Friday, June 25, 2021

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.

Read More »

An AI fair lending policy agenda for the federal financial regulators

( 31 mins) 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

Twitter
datawumi

John Merrill

Chief Technology Officer – FairPlay AI

Twitter
jwlmerrill

Lisa Rice

President and CEO – National Fair Housing Alliance

Twitter
ItsLisaRice

Kareem Saleh

Founder & CEO – FairPlay AI

Twitter
kareemsaleh

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

Related Content

Technology & Innovation
To stop algorithmic bias, we first have to define it

Emily Bembeneck, Rebecca Nissan, and Ziad Obermeyer
Thursday, October 21, 2021

Financial Regulation
Focus on bank supervision, not just bank regulation

Peter Conti-Brown and Sean Vanatta
Tuesday, November 2, 2021

Financial Regulation
Policymakers must enable consumer data rights and protections in financial services

Dan Murphy and Jennifer Tescher
Wednesday, October 20, 2021

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.

Read More »

Focus on bank supervision, not just bank regulation

( 12 mins) Introduction
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.

Related Content

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.

Read More »

Where is the Fed Vice Chair for Supervision?

( 5 mins) Randal Quarles, the first Trump appointment to the Federal Reserve’s Board of Governors, finished his four-year term as the Vice Chair for Supervision on October 13, 2021. To replace him, President Biden has nominated no one. The Fed replaced him with no one. For now, the Fed’s vital supervisory and regulatory priorities will be managed by the Fed’s Board of Governors, through their committee structure.

There is much to lament with this state of affairs. Quarles was the first to hold the position: it was created in 2010 in the Dodd-Frank Act to encourage the Fed to focus more completely on the vital work of bank regulation and supervision, areas that many feared had become neglected during the Greenspan years. Even though the position was created under a signature law of the Obama administration, that administration did not prioritize the formal appointment, relying instead on Fed Governor Tarullo to manage the portfolio, just as former Fed Governors had done. Today, for reasons known only to the administration itself, if known at all, the Biden administration has been plagued by delays in filling Fed and other financial regulatory vacancies. Even though the Vice Chair’s term is fixed by statute at four years, we still have no insight into the people the administration is even considering to succeed Quarles, as the administration has not even announced an intent to nominate anyone to any position at the Fed.
Quarles, a Republican, pursued a bank regulatory and supervisory agenda with expertise and a clear vision. He is no favorite of some Democrats, who do not endorse his vision, have little use for his expertise, and have been eager to see him depart the scene. Whether the Democrats would prefer it otherwise or not, Quarles is not going anywhere for now. He remains a Fed Governor, with the same important responsibilities over regulatory, supervisory, and monetary policy as his colleagues on the Board. That term is fixed for fourteen years and will not expire until 2032.
Here is the good news. Despite the mishandling of these vacancies from the Biden administration, the Fed’s decision not to reassign these priorities to another Governor is exactly the right thing to do. Its other alternatives are not attractive. It could have given now-Governor Quarles the responsibilities despite the expired term, but his ability to operate without the benefit of his statutory status would be significantly curtailed. The other option is hardly better: the Fed could have given these responsibilities to a candidate more in line with Democratic priorities—Fed Governor Lael Brainard, an expert on virtually every regulatory and supervisory question before the Fed, would fit this bill nicely. But Governor Brainard herself is a candidate to succeed Fed Chair Jay Powell, whose term as Chairman expires in January, and any move to reassign her portfolio could look like meddling in the Fed Chair sweepstakes that is still ongoing.
And so, the Vice Chair for Supervision—that unique creature of governance created by Congress just a decade ago—remains vacant, creating the possibility that financial regulation and supervision will not take their place at the forefront of the Fed’s policymaking. What’s more, the replacement of the Vice Chair position with a committee will devolve more authority to the Fed’s staff to handle this highly political and politicized portfolio.
So why is this good news? Because public oversight of the Federal Reserve System is primarily a product of public governance. We need, as a public, to have rigorous debates about who we want our central bankers to be. One such debate is underway as the Biden administration continues to consider the president’s appointment of the Fed Chair. Those who support Jay Powell, the incumbent, praise his leadership during the 2020 pandemic crisis and his management of a major shift in monetary policy regime. His detractors argue that his regulatory priorities are insufficiently aligned with those of the president, especially around bank regulation, financial stability, and climate change. While the tone of this debate can veer toward hyperbole—an American political tradition as old as the Republic—this is what politics looks like. We should welcome it.

Related Content

What we are not having, however, is that same level of debate around the priorities that the Fed should pursue as a regulator and supervisor. For this debate, we need to have time to consider viable candidates for this position. And we need the Fed not to do this work for us by pretending that the work of bank regulation and supervision has no political content in it.
The position obviously does have political content. The act of regulating and supervising the financial system is almost top to bottom a political exercise. We have elections to let that content and those exercises dictate the course that regulation and supervision should take. Just because the Biden administration has inexplicably dodged its responsibility for sponsoring that debate does not mean that the Fed should skip the debate entirely. By failing to appoint a successor to Quarles, the Fed has turned up the heat on the politicians to give us—the people and institutions affected most by the Fed’s regulatory and supervisory work—the chance to perform our role in vetting the nominees for this job.
Let’s hope the president accepts the Fed’s invitation as quickly as possible.

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.

Read More »

Policymakers must enable consumer data rights and protections in financial services

( 12 mins) After years of inactivity, momentum is gathering for policy action on issues related to consumer financial data in the United States. In July, the president issued an executive order encouraging the Consumer Financial Protection Bureau (CFPB) to enable data portability in financial services. The CFPB issued an advance notice of proposed rulemaking last year and expects to commence a rulemaking process in spring 2022. Congress has shown interest in the subject as well, most recently by holding a Task Force on Financial Technology hearing on consumers’ right to access financial data.

Such momentum is long overdue. Data portability in financial services has the potential to help consumers in their choice of financial service provider and enable innovation by new entrants seeking to offer a better deal or a novel product or service. While data portability is necessary to realize a more competitive and innovative financial services sector, other consumer data rights and protections are also needed. Our research indicates that consumers are demanding greater control than the current legal and regulatory framework governing financial data provides. To be responsive to these important interests, both regulatory and legislative action is needed to ensure that consumers have appropriate data rights and protections.
Background
In the wake of the global financial crisis and the ensuing public outrage over the behavior of “too big to fail” banks, policymakers in the early 2010s found themselves looking for ways to promote competition in financial services. While many debated the merits of breaking up large banks or a new Glass-Steagall Act to separate retail and investment banking, others looked for ways to promote competition from the ground up. Around the world, policymakers began to contemplate data portability measures as a way to loosen banks’ hold on dissatisfied customers.1
In the United States, this responsibility fell to the CFPB. Under Section 1033 of the Dodd Frank Wall Street Reform and Consumer Protection Act of 2010, the CFPB was empowered to prescribe rules to enable data portability in financial services.2 However, with numerous other priorities on the CFPB’s to-do list, rulemaking on Section 1033 never took place. Instead, the CFPB issued non-binding principles for data sharing and closely monitored developments in the market.
Meanwhile, consumer demand for data portability accelerated, driven by the burgeoning fintech revolution. To meet this demand, “data aggregation” companies such as Plaid began to connect consumers’ favorite fintech apps to their bank accounts. Data aggregators often used online banking login credentials shared by consumers to gain entry to consumer accounts and “screen-scrape” data available to consumers via online banking portals. Though this practice is still in use, aggregators have more recently begun to enter into contracts with banks, credit unions, core technology providers, and others to lessen dependence on credential-sharing and screen-scraping in favor of the use of tokenized account access and application program interfaces (APIs).
The financial data sharing ecosystem largely built on this technological framework has given rise to a vibrant fintech market, including many innovative companies who use consumer financial data to design products and services that help consumers improve their financial health. Today, fintechs offer products that use consumers’ financial data to help them avoid costly overdraft fees when their balances dwindle, build emergency savings when their balances grow, and optimize their bill payments to ensure that bills are paid on time without creating a liquidity shortfall. Other fintechs use cashflow data for underwriting purposes, a practice that shows evidence of increasing access to credit among those without a credit history or a credit score and those whose credit scores understate their creditworthiness.3 Still other fintechs use financial data to enable their customers to send money to friends and family within and between countries. These services are widely used, and their popularity has only increased as more and more banking activity moved online during the COVID-19 crisis.

Related Content

In early 2021, the Financial Health Network conducted a nationally representative survey to explore consumers’ interactions with, and attitudes towards, the financial data ecosystem.  According to our research, more than two thirds of banked consumers are fintech users, having linked financial apps to their checking account. In contrast with banks and credit unions,4 young people and people of color are particularly likely to use fintech apps, with apps used to send money to friends and family being the most common type of fintech app and the type of fintech app used most frequently.

The need for data portability
The lack of a comprehensive legal framework designed to govern the rights and duties of the various players in this ecosystem creates risks for individual consumers, financial institutions, and the functioning of the financial data ecosystem as a whole. Last year, the Financial Health Network partnered with FinRegLab, Flourish Ventures, and the Mitchell Sandler law firm to produce a comprehensive analysis of the legal and regulatory landscape governing consumer financial data. This analysis uncovered numerous open interpretive and policy questions related to Section 1033 as well as older statutes covering a set of interlocking issues including privacy and security under the Gramm-Leach-Bliley Act, accuracy and privacy under the Fair Credit Reporting Act, fairness under the Equal Credit Opportunity Act, and liability under the Electronic Funds Transfer Act.
Unless regulators take action, these open questions will continue to fester and have the potential to impede data portability. Already there are reports of some financial institutions restricting access to consumer data.5 Such restrictions can serve to entrench incumbent institutions and limit competition to the detriment of consumers. These restrictions also are out of step with consumer preferences. According to our research, 62 percent of consumers are in favor of data portability, believing that their bank or credit union should be required to share their personal data with another company (such as a fintech provider) if the consumer directs it to do so.
Importantly, this majority holds across demographic groups, including age, gender, education, race/ethnicity, and household income. Support for data portability in financial services is also bipartisan, with majorities of self-identified Democrats, Republicans, and Independents in favor of it.

Support for data portability holds regardless of the type of institution that serves as a consumer’s primary bank or credit union. This underscores the importance of ensuring that customers of small financial institutions with more limited technological resources have access to secure, affordable solutions to enable data portability.

These results confirm a broad consensus in favor of data portability that has been increasingly apparent for some time. Indeed, at the CFPB’s Symposium on Consumer Access to Financial Records in early 2020, few participants disputed that data portability is a right that should be available to consumers and that rulemaking on Section 1033 should guarantee.6 What they did not agree on was what other rights and protections should be guaranteed and how best to do so.
The data minimization principle
Among the issues dividing large banks, small banks, fintechs, data aggregators, and other market participants at the CFPB’s 2020 Symposium was the question of the scope. What kind of data fields should be able to be shared under Section 1033, and who should decide what kind of data are appropriate for what use case?
In the absence of regulatory guidance, the scope of data available to be shared at a consumer’s direction today varies greatly depending on where a consumer banks. Practically, this means that while some consumers currently enjoy a high degree of data portability, others have a much more limited ability to consistently share their data. As a result, consumers are unlikely to understand the scope of the data they share unless they carefully read complex legal disclosures.
The Financial Health Network asked fintech app users who had connected their fintech app to their checking account how much of their checking account data their fintech app is capable of accessing. 41 percent reported believing it could only access the data it needed, 25 percent reported believing it could access all of their checking account data, and the remaining third of respondents reported that they did not know.
When asked about how much of their checking account data fintech apps should be able to access, 87 percent reported believing that their fintech app should only be able to access the data it needs. Only 11 percent reported believing it should be able to access all the data in their checking account. In other words, consumers know what rules they want, but they are not sure if the current system is aligned with their preferences.
As with data portability, this preference for data minimization holds across demographic groups, including age, gender, education, race/ethnicity, household income, and political party affiliation. Unlike data portability, the preference for data minimization is overwhelming, with support usually in the high 80s to low 90s, with at least 75 percent of each demographic group in favor.

This indicates that while consumers desire the right to data portability, they have a strong preference for discretion as they share their data and do not wish to share any data beyond what is required for a given use case. Some data holding financial institutions (such as banks) have also emphasized this data minimization principle. However, those entities have their own competitive incentives to limit data flows and would not be impartial arbiters of what data are needed for a given use case.
With this market dynamic in mind, the CFPB should use its authority under Section 1033 to determine what data must be accessible, how often they must be made available, how long those data can be accessed for, and to whom they may be made available. If the CFPB does not feel it has the authority to impose data minimization limitations on data aggregators and recipients without impeding data portability, further legislative action may be needed to empower the Bureau to ensure that those entities are only accessing the data they need for a given use case, and are only storing that data for the minimum amount of time necessary. Congress will find strong support for this principle across the political and socio-economic spectrums.
Protecting consumers’ privacy
Consumers’ preference for discretion is not limited to the data they choose to share with fintech apps. Indeed, our research indicates that consumers are equally sensitive to financial or personal data about them being shared without their affirmative consent, no matter what institution is doing the sharing. Just as consumers do not want big tech companies sharing data about their browsing patterns without consent, consumers likewise do not want their bank or fintech app sharing financial data about them without their consent. Our survey shows consumers seem to view these forms of data sharing in much the same way, despite other research indicating that consumers have differing levels of trust for these institutions more broadly.7
Almost 90 percent of consumers (consistent among all demographic groups) expressed a preference for data sharing by their primary bank or credit union to be bound by an opt-in standard rather than an opt-out standard.

This strong preference for an opt-in standard stands in sharp contrast with current legal requirements which cannot be changed without legislative action. At present, consumers who do not want their data to be shared must opt-out, and even their ability to do that is limited. Banks are still permitted under the Gramm-Leach-Bliley Act to share consumer data with non-affiliated third parties if the information sharing is subject to one of the numerous exceptions under the law, regardless of whether a consumer might prefer them not to share.8 In other words, the current law places the burden of protecting privacy on consumers, who are expected to carefully parse complex legal disclosures provided by their financial institution and affirmatively opt-out of any optional data sharing.  According to our research, only about 1 in 4 consumers reports having done this. As low as that is, it may under-state how rare it is for consumers to opt-out of data sharing.  The plurality of banks interviewed in a 2020 study by the Government Accountability Office reported opt-out rates less than 5 percent.
In order to ensure that privacy protections are reflective of consumers’ preferences, we believe that legislative change is needed. The United States is past due for comprehensive data privacy legislation that not only addresses open issues in financial services but also ensures that consumers are afforded strong and consistent data rights and protections when they interact with tech platforms, healthcare providers, educational institutions, and others. However, if such a comprehensive effort remains beyond the reach of Congress, lawmakers should nevertheless build on the bipartisan consensus among consumers and past interest from both Republicans and Democrats in updating consumers’ data rights and protections in financial services. At the very least, data sharing by financial institutions should be bound by an opt-in standard.
Conclusion
As the financial data ecosystem evolves, regulatory and legislative action to ensure that consumers have strong data rights and protections is increasingly urgent. With momentum for action building and consumers having an unusual level of agreement on the need for data portability, data minimization, and data privacy, policymakers should proceed with the clear goal of ensuring that consumers are the primary beneficiary of the use of their financial data.

Read More »