Explainable Machine Learning in Credit Risk Management
By Niklas Bussmann, Paolo Giudici, Dimitri Marinelli, Jochen Papenbrock. Computational Economics, 2020
394 citations recorded by the source corpus.
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.
Subfields: Accounting, Finance, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/c5506b9cb68922ac262fce2545f110ecafb6f53b
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Citations in the corpus
- Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects (2021, background): “3 Bussman, Giudici, Marinelli, & Papenbrock (2019) recently proposed an explainable machine learning model specifically designed for credit risk management.”
- Systematic Literature Review (2020, methodology): “In the search, this study was limited to the year of publication between 2018-2023 and was filtered based on the appropriate title.”
- Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions (2023, background): “In other words, it becomes increasingly complex to justify the inferences of AI models, both with simpler ante-hoc explainable models [97–99], and even more with complex models [72– 74].”