Explainable Machine Learning in Credit Risk Management

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 Artifici...

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Vydáno v:Computational economics Ročník 57; číslo 1; s. 203 - 216
Hlavní autoři: Bussmann, Niklas, Giudici, Paolo, Marinelli, Dimitri, Papenbrock, Jochen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2021
Springer Nature B.V
Témata:
Artificial intelligence
Behavioral/Experimental Economics
Computer Appl. in Social and Behavioral Sciences
Credit
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
Peers
Risk management
Similarity networks
Credit risk management
Explainable AI
Financial technologies
ISSN:0927-7099, 1572-9974
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0927-7099
1572-9974
DOI:10.1007/s10614-020-10042-0