Distilling a disruptive disintermediary’s data: interpretable machine learning explanations for LendingClub customers

Thomas Conlon, Fearghal Kearney

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

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Abstract

Machine learning may assist peer-to-peer lenders in exploiting their informational advantage through distilling large volumes of data into an evaluation of borrower credit quality. In this chapter, we use explainable artificial intelligence to pare back the opacity associated with machine learning. Using LIME (Local Interpretable Model-Agnostic Explanation) and Shapley Values, we provide a visual representation of the factors found to influence credit risk for the LendingClub peer-to-peer platform. Empirical findings indicate that FICO scores are still relevant, that experienced borrowers are less risky, that loans for credit card repayments are charged more, and that administration burdens such as verifying income leads to a higher cost of credit. Our work links to ongoing regulatory initiatives by providing a mechanism to provide meaningful interpretations from machine learning models to customers, regulators, and investors.

Original languageEnglish
Title of host publicationFinTech research and applications: challenges and opportunities
EditorsDaisy Chou, Conall O'Sullivan, Vassilios G. Papavassiliou
PublisherWorld Scientific
Chapter5
Pages205-233
ISBN (Electronic)9781800612730, 9781800612723
ISBN (Print)9781800612716
DOIs
Publication statusPublished - 20 Mar 2023

Publication series

NameTransformations in Banking, Finance and Regulation
Volume5
ISSN (Print)2752-5821
ISSN (Electronic)2752-583X

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