Consumer credit in the age of AI: Beyond anti-discrimination law

Arbeitspapier

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 antidiscrimination 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.0F 1 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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SAFE Working Paper ; No. 369

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Methodological Issues: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Contract Law ; Regulated Industries and Administrative Law ; International Law ; Legal Procedure, the Legal System, and Illegal Behavior: General ; Economics of the Elderly; Economics of the Handicapped; Non-labor Market Discrimination ; Innovation and Invention: Processes and Incentives ; Technological Change: Choices and Consequences; Diffusion Processes
• Thema: credit scoring methodology ; AI enabled credit scoring ; AI borrower classification ; responsible lending ; credit scoring regulation ; financial privacy ; statistical discrimination

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Langenbucher, Katja
• Ereignis: Veröffentlichung
• (wer): Leibniz Institute for Financial Research SAFE
• (wo): Frankfurt a. M.
• (wann): 2022

Weitere Informationen

• DOI: doi:10.2139/ssrn.4298261
• Handle: http://hdl.handle.net/10419/266692
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ