Personal bankruptcy prediction using machine learning techniques

Al Daoud, E. (2019). Comparison between XGBoost, LightGBM and CatBoost using a home credit dataset. International Journal of Computer and Information Engineering, 13(1), 6–10. Search in Google Scholar

Alam, N., Gao, J., & Jones, S. (2021). Corporate failure prediction: An evaluation of deep learning vs discrete hazard models. Journal of International Financial Markets, Institutions and Money, 75, 101455. https://doi.org/10.1016/j.intfin.2021.101455 Search in Google Scholar

Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110–122. https://doi.org/10.1016/j.dss.2007.12.002 Search in Google Scholar

Altman, E. I., & Kuehne, B. J. (2016). Credit markets and bubbles: Is the benign credit cycle over? Economics and Business Review, 2(3), 20–31. https://doi.org/10.18559/ebr.2016.3.3 Search in Google Scholar

Barboza, F., Basso, L. F. C., & Kimura, H. (2021). New metrics and approaches for predicting bankruptcy. Communications in Statistics-Simulation and Computation, 52(6), 2615–2632. https://doi.org/10.1080/03610918.2021.1910837 Search in Google Scholar

Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. https://doi.org/10.1016/j.eswa.2017.04.006 Search in Google Scholar

Berlemann, M., & Salland, J. (2016). The Joneses’ income and debt market participation: Empirical evidence from bank account data. Economics Letters, 142, 6–9. https://doi.org/10.1016/j.econlet.2016.02.030 Search in Google Scholar

Bragoli, D., Ferretti, C., Ganugi, P., Marseguerra, G., Mezzogori, D., & Zammori, F. (2022). Machine-learning models for bankruptcy prediction: do industrial variables matter? Spatial Economic Analysis, 17(2), 156–177. https://doi.org/10.1080/17421772.2021.1977377 Search in Google Scholar

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. Search in Google Scholar

Brotcke, L. (2022). Time to assess bias in machine learning models for credit decisions. Journal of Risk and Financial Management, 15(4), 165. https://doi.org/10.3390/jrfm15040165 Search in Google Scholar

Brygała, M. (2022). Consumer bankruptcy prediction using balanced and imbalanced data. Risks, 10(2), 24. https://doi.org/10.3390/risks10020024 Search in Google Scholar

Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2020). Explainable AI in fintech risk management. Frontiers in Artificial Intelligence, 3, 26. https://doi.org/10.3389/frai.2020.00026 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, 101649. https://doi.org/10.1016/j.ribaf.2022.101649 Search in Google Scholar

Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 785–794. https://doi.org/10.1145/2939672.2939785 Search in Google Scholar

CFPB (Consumer Financial Protection Bureau). (2022). Is a lender allowed to consider my age or where my income comes from when deciding whether to give me a loan? https://www.consumerfinance.gov/ask-cfpb/is-a-lender-allowed-to-consider-my-age-or-where-my-income-comes-from-when-deciding-whether-to-give-me-a-loan-en-1181/ Search in Google Scholar

Coşer, A., Maer-matei, M. M., & Albu, C. (2019). Predictive models for loan default risk assessment. Economic Computation & Economic Cybernetics Studies & Research, 53(2). https://doi.org/10.24818/18423264/53.2.19.09 Search in Google Scholar

de Castro Vieira, J. R., Barboza, F., Sobreiro, V. A., & Kimura, H. (2019). Machine learning models for credit analysis improvements: Predicting low-income families’ default. Applied Soft Computing, 83, 105640. https://doi.org/10.1016/j.asoc.2019.105640 Search in Google Scholar

Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: Gradient boosting with categorical features support. https://doi.org/10.48550/arXiv.1810.11363 Search in Google Scholar

Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. https://doi.org/10.1006/jcss.1997.1504 Search in Google Scholar

Garcia, J. (2022). Bankruptcy prediction using synthetic sampling. Machine Learning with Applications, 9, 100343. https://doi.org/10.1016/j.mlwa.2022.100343 Search in Google Scholar

Georgarakos, D., Haliassos, M., & Pasini, G. (2014). Household debt and social interactions. The Review of Financial Studies, 27(5), 1404–1433. https://doi.org/10.1093/rfs/hhu014 Search in Google Scholar

Gramegna, A., & Giudici, P. (2021). SHAP and LIME: An evaluation of discriminative power in credit risk. Frontiers in Artificial Intelligence, 4, 752558. https://doi.org/10.3389/frai.2021.752558 Search in Google Scholar

Halim, Z., Shuhidan, S. M., & Sanusi, Z. M. (2021). Corporation financial distress prediction with deep learning: Analysis of public listed companies in Malaysia. Business Process Management Journal, 274), 1163–1178. https://doi.org/10.1108/bpmj-062020-0273 Search in Google Scholar

Hancock, J. T., & Khoshgoftaar, T. M. (2020). CatBoost for big data: An interdisciplinary review. Journal of Big Data, 7(1), 94. https://doi.org/10.1186/s40537-020-00369-8 Search in Google Scholar

Heo, J., & Yang, J. Y. (2014). AdaBoost based bankruptcy forecasting of Korean construction companies. Applied Soft Computing, 24, 494–499. https://doi.org/10.1016/j.asoc.2014.08.009 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, 120658. https://doi.org/10.1016/j.techfore.2021.120658 Search in Google Scholar

Jabeur, S. B., Mefteh-Wali, S., & Viviani, J. L. (2021). Forecasting gold price with the XGBoost algorithm and SHAP interaction values. Annals of Operations Research, 334, 679–699. https://doi.org/10.1007/s10479-021-04187-w Search in Google Scholar

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30. Search in Google Scholar

Khare, N., & Sait, S. Y. (2018). Credit card fraud detection using machine learning models and collating machine learning models. International Journal of Pure and Applied Mathematics, 118(20), 825–838. Search in Google Scholar

Korol, T. (2021). Examining statistical methods in forecasting financial energy of households in Poland and Taiwan. Energies, 14(7), 1821. https://doi.org/10.3390/en14071821 Search in Google Scholar

Korol, T., & Fotiadis, A. K. (2022). Implementing artificial intelligence in forecasting the risk of personal bankruptcies in Poland and Taiwan. Oeconomia Copernicana, 13(2), 407. https://doi.org/10.24136/oc.2022.013 Search in Google Scholar

Kovacova, M., Kliestik, T., Valaskova, K., Durana, P., & Juhaszova, Z. (2019). Systematic review of variables applied in bankruptcy prediction models of Visegrad group countries. Oeconomia Copernicana, 10(4), 743–772. https://doi.org/10.24136/oc.2019.034 Search in Google Scholar

Kovacova, M., & Kliestikova, J. (2017). Modelling bankruptcy prediction models in Slovak companies. SHS Web of Conferences, vol. 39, p. 01013. EDP Sciences. https://doi.org/10.1051/shsconf/20173901013 Search in Google Scholar

Le, T., Lee, M. Y., Park, J. R., & Baik, S. W. (2018). Oversampling techniques for bankruptcy prediction: Novel features from a transaction dataset. Symmetry, 10(4), 79. https://doi.org/10.3390/sym10040079 Search in Google Scholar

Letza, S. R., Kalupa, Ł., & Kowalski, T. (2003). Predicting corporate failure: How useful are multi discriminant analysis models? Economics and Business Review, 3(2), 5–11. https://doi.org/10.18559/ebr.2003.2.494 Search in Google Scholar

Liang, D., Lu, C. C., Tsai, C. F., & Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561–572. https://doi.org/10.1016/j.ejor.2016.01.012 Search in Google Scholar

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. 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, 116889. https://doi.org/10.1016/j.eswa.2022.116889 Search in Google Scholar

Mangalathu, S., Hwang, S. H., & Jeon, J. S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219, 110927. https://doi.org/10.1016/j.engstruct.2020.110927 Search in Google Scholar

Mihalovič, M. (2016). Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics & Sociology, 9(4). https://doi.org/10.14254/2071-789x.2016/9-4/6 Search in Google Scholar

Mo, H., Sun, H., Liu, J., & Wei, S. (2019). Developing window behavior models for residential buildings using XGBoost algorithm. Energy and Buildings, 205, 109564. https://doi.org/10.1016/j.enbuild.2019.109564 Search in Google Scholar

Papík, M., & Papíková, L. (2023). Impacts of crisis on SME bankruptcy prediction models’ performance. Expert Systems with Applications, 214, 119072. https:/doi.org/10.1016/j.eswa.2022.119072 Search in Google Scholar

Papík, M., Papíková, L., Kajanová, J., & Bečka, M. (2023). CatBoost: The case of bankruptcy prediction. International Conference on Business and Technology, pp. 3–17. Springer. Search in Google Scholar

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31. Search in Google Scholar

Saarela, M., & Jauhiainen, S. (2021). Comparison of feature importance measures as explanations for classification models. SN Applied Sciences, 3, 272. https://doi.org/10.1007/s42452-021-04148-9 Search in Google Scholar

Sahiq, A. N. M., Ismail, S., Nor, S. H. S., Ul-Saufie, A. Z., & Yaacob, W. F. W. (2022, September). Application of logistic regression model on imbalanced data in personal bankruptcy prediction. 2022 3rd International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp. 120–125). IEEE. https://doi.org/10.1109/aidas56890.2022.9918779 Search in Google Scholar

Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. The Stata Journal, 20(1), 3–29. https://doi.org/10.1177/1536867x20909688 Search in Google Scholar

Shi, S., Tse, R., Luo, W., D’Addona, S., & Pau, G. (2022). Machine learning-driven credit risk: A systemic review. Neural Computing and Applications, 34(17), 14327–14339. https://doi.org/10.1007/s00521-022-07472-2 Search in Google Scholar

Son, H., Hyun, C., Phan, D., & Hwang, H. J. (2019). Data analytic approach for bankruptcy prediction. Expert Systems with Applications, 138, 112816. https://doi.org/10.1016/j.eswa.2019.07.033 Search in Google Scholar

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019 Search in Google Scholar

Syed Nor, S. H., Ismail, S., & Yap, B. W. (2019). Personal bankruptcy prediction using decision tree model. Journal of Economics, Finance and Administrative Science, 24(47), 157–170. https://doi.org/10.1108/jefas-08-2018-0076 Search in Google Scholar

Wang, D. N., Li, L., & Zhao, D. (2022). Corporate finance risk prediction based on LightGBM. Information Sciences, 602, 259–268. https://doi.org/10.1016/j.ins.2022.04.058 Search in Google Scholar

Wu, D. J., Feng, T., Naehrig, M., & Lauter, K. E. (2016). Privately evaluating decision trees and random forests. Proceedings on Privacy Enhancing Technologies, (4), 335–355. https://doi.org/10.1515/popets-2016-0043 Search in Google Scholar

Yen, S. J., & Lee, Y. S. (2009). Cluster-based under-sampling approaches for imbalanced data distributions. Expert Systems with Applications, 36(3), 5718–5727. https://doi.org/10.1016/j.eswa.2008.06.108 Search in Google Scholar

Zelenkov, Y., & Volodarskiy, N. (2021). Bankruptcy prediction on the base of the unbalanced data using multi-objective selection of classifiers. Expert Systems with Applications, 185, 115559. https://doi.org/10.1016/j.eswa.2021.115559 Search in Google Scholar

Zhang, L., Wang, J., & Liu, Z. (2023). What should lenders be more concerned about? Developing a profit-driven loan default prediction model. Expert Systems with Applications, 213, 118938. https://doi.org/10.1016/j.eswa.2022.118938 Search in Google Scholar