Explainable Machine Learning in Credit Risk Management

• Published in: Computational economics Vol. 57; no. 1; pp. 203 - 216
• Main Authors: Bussmann, Niklas, Giudici, Paolo, Marinelli, Dimitri, Papenbrock, Jochen
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V
• Subjects: Artificial intelligence; Behavioral/Experimental Economics; Computer Appl. in Social and Behavioral Sciences; Credit risk; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Empirical analysis; Explainable artificial intelligence; Intelligence; Machine learning; Math Applications in Computer Science; Operations Research/Decision Theory; Risk management; Similarity networks; Credit risk management; Explainable AI; Financial technologies
• ISSN: 0927-7099; 1572-9974
• DOI: 10.1007/s10614-020-10042-0

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.