Measuring Fairness in Credit Scoring

Posted: 7 Jun 2022 Last revised: 29 Aug 2023

See all articles by Ying Chen

Ying Chen

National University of Singapore (NUS) - Department of Mathematics

Paolo Giudici

University of Pavia

Kailiang Liu

National University of Singapore (NUS) - National University of Singapore (Chongqing) Research Institute

Emanuela Raffinetti

University of Pavia

Date Written: May 30, 2022

Abstract

We propose a general methodology framework for eXplainable credit scoring to provide interpretability of each individual variable and measure fairness. Specifically, it is able to detect important variables and quantifies their individual impact on a firm’s credit classification via the Shapley-Lorenz metric; and it quantifies the degree of discrimination, conditional on the endogenous effects generated by the variables, via the Kolmogorov-Smirnov test. In the experiment on a panel dataset of 119,857 credit records for approximately 20,000 small and medium-sized enterprises (SMEs) in four European countries and 21 industry sectors for the period 2015 to 2020, we showcase the application of the eXplainable credit classification. We find that Leverage and P/L are the most important variables in credit scoring. In contrast there is marginal discrimination in terms of Country and Sector. The fairness tests show consistent results.

Keywords: Shapley-Lorenz, Artificial Intelligence Credit Scoring, Fairness Test

JEL Classification: C18, C40

Suggested Citation

Chen, Ying and Giudici, Paolo and Liu, Kailiang and Raffinetti, Emanuela, Measuring Fairness in Credit Scoring (May 30, 2022). Available at SSRN: https://ssrn.com/abstract=4123413 or http://dx.doi.org/10.2139/ssrn.4123413

Ying Chen

National University of Singapore (NUS) - Department of Mathematics ( email )

119076
Singapore

Paolo Giudici

University of Pavia ( email )

Via San Felice 7
27100 Pavia, 27100
Italy

Kailiang Liu

National University of Singapore (NUS) - National University of Singapore (Chongqing) Research Institute ( email )

Chongqing
China

Emanuela Raffinetti (Contact Author)

University of Pavia ( email )

Via San Felice 5
Pavia, 27100
Italy

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