Assessing Lending Risk: A Comparative Study of Machine Learning Algorithms
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-65203-5_86
Reference18 articles.
1. Witzany, J.: Credit risk management. In: Credit Risk Management, pp. 5–18. Springer (2017)
2. Gahlaut, A., Singh, P.K., et al.: Prediction analysis of risky credit using data mining classification models. In: 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–7 (2017)
3. Barua, S., Gavandi, D., Sangle, P., Shinde, L., Ramteke, J.: Swindle: predicting the probability of loan defaults using catboost algorithm. In: 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 1710–1715 (2021)
4. Wang, K., Li, M., Cheng, J., Zhou, X., Li, G.: Research on personal credit risk evaluation based on xgboost. Procedia Comput. Sci. 199, 1128–1135 (2022)
5. Uddin, M.S., Chi, G., Al Janabi, M.A., Habib, T.: Leveraging random forest in micro-enterprises credit risk modelling for accuracy and interpretability. Int. J. Financ. Econ. 27(3), 3713–3729 (2022)
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