The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Explainable Machine Learning in Credit Risk Management
Open accessAbstract
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.
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Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
Finding community structure in very large networks
Clauset, A., Newman, M. E. J., & Moore, C. (2004) · Physical Review E · doi:10.1103/physreve.70.066111 · Cited by 7,390
17. A Value for n-Person Games
Shapley, L. S. (1953) · Contributions to the Theory of Games (AM-28), Volume II · doi:10.1515/9781400881970-018 · Cited by 6,950
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Shapley, L. S. (1988) · The Shapley Value · doi:10.1017/cbo9780511528446.003 · Cited by 4,954
An Introduction to Econophysics: Correlations and Complexity in Finance
Mantegna, R. N., Stanley, H. E., & Chriss, N. A. (2000) · Physics Today · doi:10.1063/1.1341926 · Cited by 2,297
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · cited 2×
“In highly regulated sectors, like finance or medicine, mod‑ els should be chosen balancing accuracy with explainability (Murdoch et al. 2019).”
Introduction to Econophysics: Correlations and Complexity in Finance
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Cited by
Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J. D., Guidotti, R., Hayashi, Y., Herrera, F., Holzinger, A., et al. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102301 · Cited by 626
SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk
Gramegna, A., & Giudici, P. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.752558 · Cited by 306
Self-citationThree Challenges for AI-Assisted Decision-Making
Steyvers, M., & Kumar, A. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231181102 · Cited by 217
Interpretable machine learning for imbalanced credit scoring datasets
Chen, Y., Calabrese, R., & Martin-Barragan, B. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.036 · Cited by 190
Artificial Intelligence risk measurement
Giudici, P., Centurelli, M., & Turchetta, S. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121220 · Cited by 181
Self-citation
Metadata
- Authors
- Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J.
- Venue
- Computational Economics
- Published
- 2020-09-25
- Volume
- 57
- Issue
- 1
- Pages
- 203-216
- ISSN
- 0927-7099 (ISSN-L)
- 1572-9974
- OpenAlex
- W3000463950
- Cited by
- 503
- References
- 18
- FWCI
- 16.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable Machine Learning in Credit Risk Management. Computational Economics. https://doi.org/10.1007/s10614-020-10042-0
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