Artificial Intelligence, Machine Learning, and Bias In Finance: Toward Responsible Innovation

32 Pages Posted: 6 Dec 2019

See all articles by Kristin N. Johnson

Kristin N. Johnson

Emory University - Law School

Frank A. Pasquale

Brooklyn Law School

Jennifer Elisa Chapman (formerly Smith)

University of Maryland - Thurgood Marshall Law Library; University of Maryland Francis King Carey School of Law

Date Written: November 13, 2019

Abstract

Over the last decade, a growing number of digital startups launched bids to lure business from the financial services industry. Financial technology (“fintech”) firms deploying ever more complex and opaque algorithms assess the creditworthiness of consumers. Armed with vast quantities of data and complex algorithms to interpret the data, these firms are reigniting debates about how best to regulate financial institutions and technology firms engaged in consumer banking activities.

With a few quick taps on a smart phone, consumers can access a growing universe of apps that offer discounted interest rates on consumer loans. For proponents, the launch of fintech firms marks a new frontier in the ever-expanding utopian vision of the “technological sublime” or faith-like devotion to the potential for technology to transform us into a more equitable and just society. Consumer advocates are justifiably skeptical. While legally prohibited today, well-documented discriminatory, exclusionary, and predatory credit market practices persist.

This Essay describes fintech firms’ integration of learning algorithms and their anticipated economic and social welfare benefits — enhanced efficiency, accuracy, and accessibility. We then examine the emerging regulatory landscape. Over the last decade, federal banking regulators signaled and adopted policies that preempted state regulatory authority over fintech firms. A recent announcement by the Office of the Comptroller of the Currency (OCC) revealed the agency’s intention to allow fintech firms to apply for special purpose charters that would permit them to operate, in many respects, as national banks (“Fintech Charter Decision”).

The OCC’s Fintech Charter Decision creates gaps in the supervision of fintech firms and encourages market participants to engage in regulatory arbitrage. We argue that federal special purpose charters set the stage for regulatory arbitrage and may enable fintech firms to minimize their exposure to state antidiscrimination and consumer protection regulations. Reducing regulatory oversight of these important legal and ethical norms in a dynamic and evolving market defined by a technology that may import unconscious biases and disadvantage lower-income individuals and families raises red flags. We conclude with brief reflections regarding the necessity for courts and regulators to balance the promised benefits of fintech firms’ neo-banking initiatives with the historic and special gatekeeping role of banking platforms. Unilateral deregulatory action by state or federal regulators may undermine efforts to ensure effective oversight of fintech firms that seek to extend access to safe and affordable banking services.

Keywords: Technology, Artificial Intelligence, Finance, Banking, Algorithms, Bias

Suggested Citation

Johnson, Kristin N. and Pasquale, Frank A. and Chapman, Jennifer Elisa, Artificial Intelligence, Machine Learning, and Bias In Finance: Toward Responsible Innovation (November 13, 2019). Fordham Law Review, Vol. 88, No. 4, 2019, Tulane Public Law Research Paper No. 19-6, Available at SSRN: https://ssrn.com/abstract=3486441

Kristin N. Johnson (Contact Author)

Emory University - Law School ( email )

1301 Clifton Road, N.E.
Atlanta, GA USA 30306
United States

Frank A. Pasquale

Brooklyn Law School ( email )

250 Joralemon Street
Brooklyn, NY 11201
United States

Jennifer Elisa Chapman

University of Maryland - Thurgood Marshall Law Library ( email )

501 West Fayette Street
Baltimore, MD 21201
United States

University of Maryland Francis King Carey School of Law ( email )

500 West Baltimore Street
Baltimore, MD 21201-1786
United States

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