Consumer credit in the age of AI – beyond anti-discrimination law

  • Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.

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Author:Katja LangenbucherORCiDGND
Parent Title (English):LawFin working paper ; No. 42, ECGI Working Paper Series in Law ; Working Paper N° 663/2022
Series (Serial Number):LawFin Working Paper (42)
Publisher:Center for Advanced Studies on the Foundations of Law and Finance, House of Finance, Goethe University
Place of publication:Frankfurt am Main
Document Type:Working Paper
Year of Completion:2022
Year of first Publication:2022
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/12/12
Tag:AI borrower classification; AI enabled credit scoring; credit scoring methodology; credit scoring regulation; financial privacy; responsible lending; statistical discrimination
Edition:November 2022
Page Number:67
JEL-Klassifikation: C18 Methodological Issues: General
Institutes:Rechtswissenschaft / Rechtswissenschaft
Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF)
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE)
Wissenschaftliche Zentren und koordinierte Programme / DFG-Forschergruppen / Foundation of Law and Finance
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
3 Sozialwissenschaften / 34 Recht / 340 Recht
JEL-Classification:C Mathematical and Quantitative Methods / C3 Multiple or Simultaneous Equation Models / C32 Time-Series Models; Dynamic Quantile Regressions (Updated!)
J Labor and Demographic Economics / J1 Demographic Economics / J14 Economics of the Elderly; Economics of the Handicapped
K Law and Economics / K1 Basic Areas of Law / K12 Contract Law
K Law and Economics / K3 Other Substantive Areas of Law / K33 International Law
K Law and Economics / K4 Legal Procedure, the Legal System, and Illegal Behavior / K40 General
O Economic Development, Technological Change, and Growth / O3 Technological Change; Research and Development / O31 Innovation and Invention: Processes and Incentives
O Economic Development, Technological Change, and Growth / O3 Technological Change; Research and Development / O33 Technological Change: Choices and Consequences; Diffusion Processes
Licence (German):License LogoDeutsches Urheberrecht