A Quantitative Analysis of Default Risk Using Machine Learning and SHAP Value Interpretation
References
- Bellotti A., Brigo D., Gambetti P., Vrins F. (2021). Forecasting recovery rates on non-performing loans with machine learning.
International Journal of Forecasting , 37(1), 428-444. - Carmona P., Dwekat A., Mardawi Z. (2022). No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure.
Research in International Business and Finance, 61. - Pang S., Hou X., Xia L. (2021). Borrowers’ credit quality scoring model and applications, with default discriminant analysis based on the extreme learning machine.
Technological Forecasting and Social Change , 165. - Jabeur S.B., Gharib C., Mefteh-Wali S., Arfi W.B. (2021). CatBoost model and artificial intelligence techniques for corporate failure prediction.
Technological Forecasting and Social Change , 166. - Li A., He J., Liu Z. (2022). An Information Based Fuzzy Partitioning Approach (IBFP) for “Bad” Credit Detection.
Procedia Computer Science , 199, 1160-1167. - Yildirim M., Okay F.Y., Özdemir S. (2021). Big data analytics for default prediction using graph theory.
Expert Systems with Applications , 176. - Machado M.R., Karray S. (2022). Assessing credit risk of commercial customers using hybrid machine learning algorithms.
Expert Systems with Applications , 200. - Lappas P.Z., Yannacopoulos A.N. (2021). A machine learning approach combining expert knowledge with genetic algorithms in feature selection for credit risk assessment.
Applied Soft Computing , 107, 428-444. - Zanin L. (2020). Combining multiple probability predictions in the presence of class imbalance to discriminate between potential bad and good borrowers in the peer-to-peer lending market.
Journal of Behavioral and Experimental Finance , 25. - Saurabh A., Sushant B., Survesh S., Nassa V.K. (2022). Prediction of credit card defaults through data analysis and machine learning techniques.
Materials Today: Proceedings , 51(1), 110-117. - Bashar A., Nayak R., Astin-Walmsley K., Heath K. (2021). Machine learning for predicting propensity-to-pay energy bills.
Intelligent Systems with Applications , 17. - Schoonbe, L., Moore, W.R., Van Vuuren, J.H. (2022). A machine-learning approach towards solving the invoice payment prediction problem.
South African Journal of Industrial Engineering , 33(4), 126-146. - Nst, A.R., Sari, M.M., Ramadhani, A., Maramis, B.C., Nst, H.K., & Ghazali, M.R. (2023). Analysis of factors affecting bad debts.
World Journal of Advanced Research and Reviews . - Ariza-Garzón M.J., Arroyo J., A. Caparrini and M. -J. Segovia-Vargas. (2020). Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending.
IEEE Access , 8, 2169-3536 (Online).
DOI: https://doi.org/10.2478/picbe-2024-0020 | Journal eISSN: 2558-9652
Language: English
Page range: 233 - 245
Published on: Jul 3, 2024
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
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© 2024 Coralia Tanasuica Zotic, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution 4.0 License.