THE NORMS OF ALGORITHMIC CREDIT SCORING
Nikita Aggarwal
Abstract
Abstract This article examines the growth of algorithmic credit scoring and its implications 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.