When all data is credit data: consumer credit markets, technological development, and distributive justice

Thesis

This thesis examines how advances in predictive technology influence the distributional outcomes due to consumer credit markets, using ‘alternative’ consumer credit scoring as a case study. The thesis contributes the first scholarly analysis of the distributional effects due to alternative credit scoring in the UK, and the role of legal, technological, political, and market forces in shaping these effects. It also contributes to the deeper and broader analysis of the economic and social outcomes due to consumer credit markets, and the boundaries of ‘fair’ credit.

Alternative credit scoring—the use of alternative data and machine learning techniques in consumer credit decisions—has been heralded with the implicitly distributional promise of improving access to credit for marginalized consumers, particularly lower-income consumers. Leveraging theoretical and empirical insights, the thesis argues that this promise is credible but strictly bounded. First, due to the limits of consumer credit, particularly unsecured credit, as a mechanism for reducing poverty and inequality. Second, due to the potential negative distributional effects of alternative credit scoring—whether resulting from more precise, data-driven price discrimination targeted at lower-income consumers, or the expansion of affordable credit to higher-income consumers.

Further empirical investigation is needed to estimate the distributional outcomes due to alternative credit scoring, and advances in predictive credit technology more broadly, as well as the mechanisms producing these outcomes, particularly in the UK. To the extent that regressive outcomes are at least plausible, the thesis sketches the contours of policy interventions that could more effectively limit these outcomes and foster more progressive outcomes due to technological development in consumer credit markets.

Authors: Aggarwal, N

• DOI: 10.5287/ora-j1gxdawvq
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: credit reporting; information; innovation; consumer finance; alternative credit scoring; artificial intelligence; distributional fairness; consumer credit regulation; algorithmic credit scoring; machine learning; privacy; algorithms; consumer credit; credit scoring; digital; data; regulation; fintech; data protection regulation; financial markets; data protection; technological change; distributive justice
• Subjects: Law