Machine Learning, Big Data and the Regulation of Consumer Credit Markets: The Case of Algorithmic Credit Scoring

N Aggarwal, H Eidenmüller, L Enriques, J Payne, K van Zwieten (eds) Autonomous Systems and the Law (Beck 2019)

6 Pages Posted: 8 Jan 2020

See all articles by Nikita Aggarwal

Nikita Aggarwal

University of Oxford - Oxford Internet Institute; University of Oxford, Faculty of Law

Date Written: November 1, 2018

Abstract

Recent advances in machine learning (ML) and Big Data techniques have facilitated the development of more sophisticated, automated consumer credit scoring models — a trend referred to as 'algorithmic credit scoring' in recognition of the increasing reliance on computer (particularly ML) algorithms for credit scoring. This chapter, which forms part of the 2018 collection of short essays 'Autonomous Systems and the Law', examines the rise of algorithmic credit scoring, and considers its implications for the regulation of consumer creditworthiness assessment and consumer credit markets more broadly.

The chapter argues that algorithmic credit scoring, and the Big Data and ML technologies underlying it, offer both benefits and risks for consumer credit markets. On the one hand, it could increase allocative efficiency and distributional fairness in these markets, by widening access to, and lowering the cost of, credit, particularly for 'thin-file' and 'no-file' consumers. On the other hand, algorithmic credit scoring could undermine distributional fairness and efficiency, by perpetuating discrimination in lending against certain groups and by enabling the more effective exploitation of borrowers.

The chapter considers how consumer financial regulation should respond to these risks, focusing on the UK/EU regulatory framework. As a general matter, it argues that the broadly principles and conduct-based approach of UK consumer credit regulation provides the flexibility necessary for regulators and market participants to respond dynamically to these risks. However, this approach could be enhanced through the introduction of more robust product oversight and governance requirements for firms in relation to their use of ML systems and processes. Supervisory authorities could also themselves make greater use of ML and Big Data techniques in order to strengthen the supervision of consumer credit firms.

Finally, the chapter notes that cross-sectoral data protection regulation, recently updated in the EU under the GDPR, offers an important avenue to mitigate risks to consumers arising from the use of their personal data. However, further guidance is needed on the application and scope of this regime in the consumer financial context.

Keywords: Machine Learning, Artificial Intelligence, Big Data, Consumer Finance, Credit Scoring, Algorithmic Accountability, Financial Regulation, Data Protection, Law and Technology

JEL Classification: K20, K12

Suggested Citation

Aggarwal, Nikita, Machine Learning, Big Data and the Regulation of Consumer Credit Markets: The Case of Algorithmic Credit Scoring (November 1, 2018). N Aggarwal, H Eidenmüller, L Enriques, J Payne, K van Zwieten (eds) Autonomous Systems and the Law (Beck 2019). Available at SSRN: https://ssrn.com/abstract=3309244 or http://dx.doi.org/10.2139/ssrn.3309244

Nikita Aggarwal (Contact Author)

University of Oxford - Oxford Internet Institute ( email )

1 St. Giles
University of Oxford
Oxford OX1 3PG Oxfordshire, Oxfordshire OX1 3JS
United Kingdom

University of Oxford, Faculty of Law ( email )

St Cross Building
St Cross Rd
Oxford
United Kingdom

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