Machine Learning in Consumer Credit: Legal, Economic, Ethical & Policy Implications

Consumer credit plays a vital role in economic opportunity and social mobility, yet it has long been plagued by inefficiencies, inequalities, and inadequate regulatory protection. As consumer credit markets evolve in the 21st century, the integration of artificial intelligence and machine learning offers potential solutions to these persistent challenges, but also risks exacerbating existing problems. This research challenges pervasive binary assumptions that the introduction of artificial intelligence will either inherently improve decision-making for the better or only result in harm to end-users. Through an interdisciplinary and multifaceted survey of economic, normative, policy, and legal issues, this thesis evaluates existing laws and proposes recommendations for future law reform, optimal technical design of machine learning models, and guidance on regulating for fair machine learning in consumer lending. Chapter 1 introduces the motivations, research aims, scope, and methodology, providing an overview of artificial intelligence and machine learning relevant to this thesis. By formulating supervised machine learning through a statistical decision-theoretic framework, this chapter contributes a meaningful framework to examine the technical design choices of predictive models. Chapter 2 undertakes an economic analysis of the tensions between financial institutions and consumers. It argues that well-designed machine learning systems can overcome existing inefficiencies and asymmetries in consumer credit markets. Chapter 3 defines core moral approaches to machine learning and connects optimal economic outcomes with optimal normative outcomes. It proposes a novel approach to balancing tensions between consequentialism and deontology in the context of machine learning design. Chapter 4 presents a comparative legal analysis of the laws in the United States and United Kingdom that regulate the use of algorithmic decision-making in consumer credit. This analysis highlights gaps between how law traditionally applies to consumer lending and how it may be challenged by algorithmic lending processes. Chapter 5 evaluates the adequacy of current legal approaches identified in Chapter 4 to promote optimal economic and normative outcomes. It develops principles and recommendations to achieve positive outcomes for individuals, firms, and society.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Sargeant, Holli

Advisors dc:contributor.advisor

• Steffek, felix
• Vinx, lars

• Law and AI
• Law and Machine Learning

Licence

• https://www.repository.cam.ac.uk/bitstreams/e7a11ad7-fa86-41bd-bd82-6d3a7e3aa2c6/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

Author Identifier

0000-0003-3482-7789

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/379363