The Norms of Algorithmic Credit Scoring

19 Pages Posted: 2 May 2020

See all articles by Nikita Aggarwal

Nikita Aggarwal

University of Oxford - Oxford Internet Institute; University of Oxford, Faculty of Law; European Corporate Governance Institute (ECGI)

Date Written: April 5, 2020

Abstract

This article examines the growing use of alternative data and machine learning to assess consumer creditworthiness — a trend described as ‘algorithmic credit scoring’ — and the implications of this trend for the regulation of consumer credit markets in the UK. It frames the analysis of algorithmic credit scoring in terms of three, core regulatory norms: allocative efficiency, distributional fairness, and consumer autonomy (privacy). Examining the normative trade-offs that arise within this frame, the article argues that the existing regulatory framework governing algorithmic credit scoring does not achieve a satisfactory normative balance. In particular, the growing reliance on consumers’ personal data and behavioral profiling by lenders due to algorithmic credit scoring, coupled with the ineffectiveness of individualized, rights- and market-based mechanisms under existing data protection regulation, present a significant threat to consumers’ privacy and autonomy in consumer credit markets. The article concludes with a recommendation for stricter limits on the processing of (personal) data in the context of consumer lending.

Keywords: Algorithmic Credit Scoring, Algorithmic Decision-Making, Artificial Intelligence, Machine Learning, Privacy Law, Data Protection Law, GDPR, Consumer Finance Law, Law and Technology, Financial Regulation

Suggested Citation

Aggarwal, Nikita, The Norms of Algorithmic Credit Scoring (April 5, 2020). Available at SSRN: https://ssrn.com/abstract=3569083 or http://dx.doi.org/10.2139/ssrn.3569083

Nikita Aggarwal (Contact Author)

University of Oxford - Oxford Internet Institute ( email )

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University of Oxford, Faculty of Law ( email )

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European Corporate Governance Institute (ECGI) ( email )

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