Machine Learning in Consumer Credit: Legal, Economic, Ethical & Policy Implications
type: CreativeWork
description: 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 highli…
abstract: 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 highli…
author: Sargeant, Holli
language: eng
keywords: Law and AI, Law and Machine Learning
license: https://www.repository.cam.ac.uk/bitstreams/e7a11ad7-fa86-41bd-bd82-6d3a7e3aa2c6/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2024-08-01
date modified: 2026-07-22T22:24:06Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.115485