Capotorti, A., & Barbanera, E. (2012). Credit scoring analysis using a fuzzy probabilistic rough set model. <em>Computational Statistics & Data Analysis</em>, <em>56</em>(4), 981-994.
Li, Z., Tian, Y., Li, K., Zhou, F., & Yang, W. (2017). Reject inference in credit scoring using semi-supervised support vector machines. Expert Systems with Applications, 74, 105-114.
Shi, S., Tse, R., Luo, W., D’Addona, S., & Pau, G. (2022). Machine learning-driven credit risk: a systemic review. <em>Neural Computing and Applications</em>, <em>34</em>(17), 14327-14339.
Uddin, M. S., Chi, G., Al Janabi, M. A., & Habib, T. (2022). Leveraging random forest in micro‐ enterprises credit risk modelling for accuracy and interpretability. <em>International Journal of Finance & Economics</em>, <em>27</em>(3), 3713-3729.
Wang, Y., Zhang, Y., Lu, Y., & Yu, X. (2020). A Comparative Assessment of Credit Risk Model Based on Machine Learning – a case study of bank loan data. <em>Procedia Computer Science</em>, <em>174</em>, 141-149.
Zamore, S., Ohene Djan, K., Alon, I., & Hobdari, B. (2018). Credit risk research: Review and agenda. <em>Emerging Markets Finance and Trade</em>, <em>54</em>(4), 811-835.
Zekic-Susac, M., Sarlija, N., & Bensic, M. (2004, June). Small business credit scoring: a comparison of logistic regression, neural network, and decision tree models. In <em>26</em><sup>th</sup> <em>International Conference on Information Technology Interfaces, 2004.</em> (pp. 265-270). IEEE.