Category: Recommended Reads

Big Data and Discrimination

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This essay discusses the legal requirements of pricing credit and the architecture of machine learning and intelligent algorithms to provide an overview of legislative gaps, legal solutions, and a framework for testing discrimination that evaluates algorithmic pricing rules. Using real-world mortgage data, the authors find that restricting the data characteristics within the algorithm can increase pricing gaps while having a limited impact on disparity.

Talia B. Gillis & Jann L. Spiess, University of Chicago Law Review

Big Data’s Disparate Impact

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This paper examines concerns about big data’s disparate impact risk from the perspective of American antidiscrimination law, more specifically, through Title VII’s prohibition of discrimination in employment. The paper also calls out the legal and political difficulties of addressing and remedying this type of discrimination, in particular, the tension between the two major theories underlying antidiscrimination law: anticlassification and antisubordination.

104 California Law Review 671 (2016)

Solon Barocas, Andrew D. Selbst

Winners and Losers of Marketplace Lending: Evidence From Borrower Credit Dynamics

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This paper studies outcomes for borrowers who take out credit card consolidation loans from marketplace lenders, finding evidence some borrowers are left worse off.

Sudheer Chava, Nikhil Paradkar

Big data meets artificial intelligence

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This paper provides an overview of the challenges and implications for the supervision and regulation of financial services with regards to the opportunities presented by BDAI technology: the phenomena of big data (BD) being used in conjunction with artificial intelligence (AI). The paper draws from market analyses and use cases to outline potential developments seen from the industry and government perspectives, and the impact on consumers.

Germany’s Federal Financial Supervisory Authority (BaFin)

On the Rise of the FinTechs—Credit Scoring using Digital Footprints

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This paper evaluates users’ digital footprints or the information that people leave online by accessing or registering a website to predict consumers’ likelihood of default. Using more than 250,000 observations, the authors show that information gleaned from people’s digital footprint can be equal to or even exceed the information content of credit bureau scores.

FDIC Center for Financial Research Working Paper No. 2018-04

Tobias Berg, Valentin Burg, Ana Gombovic and Manju Puri

Data point: The geography of credit invisibility

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This paper examines geographic patterns to assess the extent to which where one resides is correlated with one’s likelihood of remaining credit invisible. The paper explores the following topics: credit deserts, credit invisibility in rural and urban areas, entry products by geography, and credit invisibility.

The Bureau of Consumer Financial Protection’s Office of Research

Kenneth Brevoort, Jasper Clarkberg, Michelle Kambara, and Benjamin Litwin.

Regulating a Revolution: From Regulatory Sandboxes to Smart Regulation

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This paper addresses the challenge to the current financial regulatory regime from fintech firms and data-driven financial service providers. The authors consider new regulatory approaches and propose a a new type of regulatory supervision called ‘smart’ regulation and provides a roadmap to become digitized, and then build digitally-smart regulation.

23 Fordham Journal of Corporate and Financial Law 31-103 (2017)

Dirk A. Zetzsche, Ross P. Buckley, Janos N. Barberis, Douglas W. Arner.

What Do a Million Observations Have to Say About Loan Defaults? Opening the Black Box of Relationships

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The paper evaluates the impact of aspects of customer-bank relationships on loan default rates. Using a dataset that consists of more than 1 million loans made by 296 German banks, the research finds that banks with relationship-specific information from customers establishing transaction accounts are less likely to experience loan defaults and act differently in screening and monitoring behaviors than banks with no information.

Manju Puri, Jörg Rocholl, Sascha Steffen

Predictably Unequal? The Effects of Machine Learning on Credit Markets

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This paper uses various types of machine learning models to predict credit risk using historical mortgage data. It finds gains in predictiveness that would likely lead to an increase in approvals across all demographic groups, but that average prices could increase for African-American and Hispanic borrowers due to differences in risk calculations.

Andreas Fuster, Paul Goldsmith-Pinkham, Tarun Ramadorai, and Ansgar Walther.

Consumer Lending Discrimination in the FinTech Era

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This paper evaluates differentials between borrowers of different races in loan approval rates and pricing between fintech and traditional mortgage lenders. The paper finds that unexplained pricing differentials are smaller among technology-heavy lenders and that such differentials overall have declined as the mortgage industry as increased reliance on algorithmic lending in recent years.

UC Berkeley Public Law Research Paper

Robert Bartlett, Adair Morse, Richard Stanton, Nancy Wallace.

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