Artificial Intelligence: Recommendations for Principled Modernization of the Regulatory Framework

This report contains recommendations for financial services policymakers for a principled modernization of the regulatory framework to facilitate the responsible use of artificial intelligence in credit underwriting.

Artificial intelligence (“AI”) offers a leap forward for the accuracy and fairness of decisions on consumer credit.  AI can integrate and analyze richer data sets than conventional credit underwriting, and can more accurately assess a consumer’s creditworthiness using factors (and combinations of factors) not considered by conventional underwriting systems.  This increased accuracy will benefit borrowers who currently face obstacles obtaining low-cost bank credit under conventional underwriting approaches.

There is no universally accepted definition of AI.[1]  In general, AI is associated with the development and implementation of computer systems to perform tasks that traditionally would have required human cognitive intelligence, such as thinking and decision-making.[2]  Machine learning is a subset of AI that generally refers to the ability of a software algorithm to identify patterns and automatically optimize and refine performance from processing large data sets with little or no human intervention or programming.[3]  Although AI has existed for many years, interest in applying AI has surged as a result of increases in computing power and the availability of large data sets, including, in the financial services sector, “alternative data” not traditionally collected by consumer reporting agencies or used in calculating credit scores.[4]  For simplicity, this white paper uses the term “AI” to refer to the evaluation of large data sets using machine learning algorithms.

Much of the current regulatory framework was devised well before AI was used to assist credit underwriting.  Unsurprisingly, that framework is now outdated in ways that constrain the transformative power of AI.  The Bank Policy Institute (“BPI”) and the law firm of Covington & Burling LLP (“Covington”)[5] have prepared this white paper to:

  • explain the regulatory framework that currently applies to the use of AI in credit underwriting;
  • identify the ways in which that regulatory framework impedes broad implementation of AI in credit underwriting; and
  • provide BPI’s recommendations for a regulatory process designed to modernize the regulatory framework in a way that preserves core regulatory principles while removing unnecessary obstacles to the use of AI to improve credit underwriting.

This white paper focuses principally on the regulatory frameworks relating to fair lending and model risk management, as these two areas bear significantly on banks’ efforts to implement AI systems in credit underwriting.  Although this paper focuses on credit underwriting, the proposed regulatory modernization would also facilitate the use of AI systems in related areas, such as marketing, customer service, and collections.

BPI recommends that the Consumer Financial Protection Bureau (“CFPB”) and the federal banking agencies[6] work together to identify obstacles to the responsible use of AI in credit underwriting and implement a principled modernization of the existing regulatory framework to eliminate those obstacles.  To succeed and foster the responsible use of AI in credit underwriting, principled modernization should include each of the following six elements:

  • Coordination. Principled modernization should involve a coordinated, interagency effort to develop a consistent set of expectations for the use of AI in credit underwriting.  Such an effort would promote both consumer protection and the safety and soundness of financial institutions.
  • Preservation of Regulatory Principles. Principled modernization should preserve critical regulatory principles, such as the prohibition against unlawful discrimination, while critically examining and updating regulatory practices that may unintentionally discourage bank adoption of new technologies.
  • Recognition of the Distinct Features of AI. Principled modernization should include targeted changes to regulatory practices to take into account the distinct features of AI models and to place AI and traditional models on an equal regulatory footing.  Such changes should create substantial flexibility going forward in light of the pace of technological change and the risk that prescriptive changes may have unintended consequences.
  • Level Playing Field. Principled modernization should create a regulatory framework that applies equally to banks and non-banks.  A level playing field gives consumers the broadest possible opportunities to obtain credit and promotes fair treatment of consumers by all creditors.
  • Consistency. Principled modernization should result in a uniform regulatory framework that is applied consistently by all federal financial regulatory agencies.
  • Transparency. Principled modernization should yield a regulatory framework that is made wholly transparent through one or more regulatory publications, whether issued jointly or in consultation and coordination among the agencies, so that all stakeholders understand how AI may be used in credit underwriting.

Consistent with the foregoing elements, BPI also recommends that the CFPB and the federal banking agencies follow the principles set forth in the Memorandum issued by the Office of Management and Budget (“OMB”) in January 2020 containing proposed Guidance for Regulation of Artificial Intelligence Applications, and submit to OMB plans for achieving consistency with the Guidance.[7]  Modernizing existing regulatory approaches as described above would allow more creditors to utilize AI in credit underwriting, provide consistent consumer protection, strengthen safe and sound underwriting practices, and foster responsible and fair outcomes.

This white paper is organized as follows:

  • Section I describes the promise of AI in improving credit underwriting.
  • Section II reviews the current state of the law relating to credit underwriting.
  • Section III provides recommendations for modernization of the regulatory framework to facilitate the responsible use of AI in credit underwriting while preserving core regulatory principles and outlines potential regulatory areas that may be candidates for modernization.

To read the full white paper, please click the “Download PDF” link below.

[1] See National Institute of Standards and Technology, U.S. Leadership in AI:  A Plan for Federal Engagement in Developing Technical Standards and Related Tools at 7-8 (Aug. 9, 2019), (noting that “definitions of AI vary”); Executive Office of the President National Science and Technology Council Committee on Technology, Preparing for the Future of Artificial Intelligence, at 6 (Oct. 2016),

[2] See U.S. Dep’t of the Treasury, A Financial System That Creates Opportunities: Nonbank Financials, Fintech, and Innovation, at 53 (July 2018),—Nonbank-Financi….pdf; Financial Stability Board, Artificial intelligence and machine learning in financial services – Market developments and financial stability implications, at 4 (Nov. 1, 2017),

[3] See U.S. Dep’t of the Treasury, A Financial System That Creates Opportunities, at 53 (July 2018),—Nonbank-Financi….pdf; Financial Stability Board, Artificial intelligence and machine learning in financial services – Market developments and financial stability implications, at 4 (Nov. 1, 2017),; Governor Lael Brainard, What Are We Learning about Artificial Intelligence in Financial Services?, speech at Fintech and the New Financial Landscape, Philadelphia, Pennsylvania(Nov. 13, 2018),

[4] See Financial Stability Board, Artificial intelligence and machine learning in financial services – Market developments and financial stability implications, at 3-4 (Nov. 1, 2017),; see also CFPB, Request for Information Regarding Use of Alternative Data and Modeling Techniques in the Credit Process, 82 Fed. Reg. 11,183, 11,184 (Feb. 21, 2017), (“Alternative data” refers to any data that are not “traditional.”  We use “alternative” in a descriptive rather than normative sense and recognize there may not be an easily definable line between traditional and alternative data”); U.S. Government Accountability Office, Financial Technology: Agencies Should Provide Clarification on Lender’s Use of Alternative Data, GAO-19-111 at 33 (Dec. 2018), (“. . . alternative data is any information not traditionally used by the three national consumer reporting agencies when calculating a credit score”).

[5] This white paper was jointly prepared by BPI and Covington.  BPI is a nonpartisan public policy, research and advocacy group, representing the nation’s leading banks.  BPI’s members include national banks, regional banks and major foreign banks doing business in the United States.  Collectively, they employ nearly 2 million Americans, make 72% of all loans and nearly half of the nation’s small business loans, and serve as an engine for financial innovation and economic growth.  Covington is an international law firm headquartered in Washington, D.C. that advises and represents a wide range of financial institutions and other clients.

[6] The federal banking agencies are the Board of Governors of the Federal Reserve System (“FRB”), Federal Deposit Insurance Corporation (“FDIC”), National Credit Union Administration (“NCUA”), and Office of the Comptroller of the Currency (“OCC”).

[7] Office of Management and Budget, Memorandum for the Heads of Executive Departments and Agencies, Guidance for Regulation of Artificial Intelligence Applications (Jan. 7, 2020), (hereafter “OMB Memorandum”); see also 85 Fed. Reg. 1825 (Jan. 13, 2020) (requesting public comment on the draft Memorandum).