Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction
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DOI: 10.1186/s40854-022-00366-1
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Cited by:
- Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
- González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
- Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
- Ryuichiro Hashimoto & Kakeru Miura & Yasunori Yoshizaki, 2023. "Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning," Bank of Japan Working Paper Series 23-E-6, Bank of Japan.
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Keywords
Artificial intelligence; Machine learning; Credit risk; Interpretability; Bias; Internal ratings based model; IRB model; Natural language processing; NLP;All these keywords.
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