Download This PaperThe Norms of Algorithmic Credit Scoring
80 Cambridge Law Journal 42 (2021) (published version available at https://bit.ly/3FRPupd)
24 Pages Posted: 2 May 2020 Last revised: 28 Mar 2023
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 constructs a frame of analysis for the regulation of algorithmic credit scoring, bound by the core norms underpinning UK consumer credit and data protection regulation: allocative efficiency, distributional fairness and consumer privacy (as autonomy). Examining the normative trade-offs that arise within this frame, the article argues that existing data protection and consumer credit frameworks do not achieve an appropriate normative balance in the regulation of algorithmic credit scoring. In particular, the growing reliance on consumers’ personal data by lenders due to algorithmic credit scoring, coupled with the ineffectiveness of existing data protection remedies has created a data protection gap in consumer credit markets that presents a significant threat to consumer privacy and autonomy. The article makes recommendations for filling this gap through institutional and substantive regulatory reforms.
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
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