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Explainable prediction of loan default based on machine learning models

Zhu, Xu; Chu, Qingyong; Song, Xinchang; Hu, Ping; Peng, Lu - In: Data science and management : DSM 6 (2023) 3, pp. 123-133

thereby increase the user's faith in the model, an explanatory model must be used. Logistic regression, decision tree, XGBoost …, and LightGBM models are employed to predict a loan default. The prediction results show that LightGBM and XGBoost … 0.7213. The accuracies of LightGBM and XGBoost exceed 0.8. The precisions of LightGBM and XGBoost exceed 0 …

Persistent link: https://www.econbiz.de/10014518009

A novel default risk prediction and feature importance analysis technique for marketplace lending using machine learning

Imam, Sana Hassan; Huhn, Sebastian; Hornuf, Lars; … - In: Credit and capital markets : Kredit und Kapital 56 (2023) 1, pp. 27-62

Marketplace lending has fundamentally changed the relationship between borrowers and lenders in financial markets. As with many other financial products that have emerged in recent years, internet-based investors may be inexperienced in marketplace lending, highlighting the importance of...

Persistent link: https://www.econbiz.de/10014518604

Understanding corporate bond defaults in Korea using machine learning models

Park, Dojoon; Auh, Jun Kyung; Song, Giwan; Eom, Young Ho - In: Asia-Pacific journal of financial studies 53 (2024) 2, pp. 238-276

Persistent link: https://www.econbiz.de/10014530710

Machine Learning for Corporate Default Risk : Multi-Period Prediction, Frailty Correlation, Loan Portfolios, and Tail Probabilities

Sigrist, Fabio; Leuenberger, Nicola - 2021

We use machine learning methods for modeling multi-period corporate default probabilities and obtain higher prediction accuracy compared to linear models with the differences being larger for longer prediction horizons. Overall, tree-boosting has the highest prediction accuracy. In addition, we...

Persistent link: https://www.econbiz.de/10013324358