[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Li A., He J., Liu Z. (2022). An Information Based Fuzzy Partitioning Approach (IBFP) for “Bad” Credit Detection. Procedia Computer Science, 199, 1160-1167.
]Search in Google Scholar
[
Yildirim M., Okay F.Y., Özdemir S. (2021). Big data analytics for default prediction using graph theory. Expert Systems with Applications, 176.
]Search in Google Scholar
[
Machado M.R., Karray S. (2022). Assessing credit risk of commercial customers using hybrid machine learning algorithms. Expert Systems with Applications, 200.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
Bashar A., Nayak R., Astin-Walmsley K., Heath K. (2021). Machine learning for predicting propensity-to-pay energy bills. Intelligent Systems with Applications, 17.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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).
]Search in Google Scholar