Explainable Machine Learning in Credit Risk Management

2020/09/25 by Niklas Bussmann, Paolo Giudici, Dimitri Marinelli +1 · 398 citations
Business, Management and Accounting · Economics, Econometrics and Finance · #Credit Risk and Financial Regulations #FinTech, Crowdfunding, Digital Finance #Financial Distress and Bankruptcy Prediction

paper · pdf · doi:10.1007/s10614-020-10042-0

published in Computational Economics 57(1), 203-216 (Springer Science and Business Media LLC)

crossref issued 2020/09/25 · crossref published 2020/09/25 · crossref published-online 2020/09/25 · openalex publication_date 2020/09/25 · crossref created 2020/09/25 · crossref published-print 2021/01/01 · crossref deposited 2021/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01 · crossref indexed 2026/08/08

Abstract

Abstract The paper proposes an explainable Artificial Intelligence model that can be used in credit risk management and, in particular, in measuring the risks that arise when credit is borrowed employing peer to peer lending platforms. The model applies correlation networks to Shapley values so that Artificial Intelligence predictions are grouped according to the similarity in the underlying explanations. The empirical analysis of 15,000 small and medium companies asking for credit reveals that both risky and not risky borrowers can be grouped according to a set of similar financial characteristics, which can be employed to explain their credit score and, therefore, to predict their future behaviour.

Citations

• A Unified Approach to Interpreting Model Predictions
• From local explanations to global understanding with explainable AI for trees

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