TIJORAT BANKLARIDA MIJOZLARNING KREDIT QOBILIYATINI BAHOLASH UCHUN SKORING MODELIDA SUN’IY INTELLEKTNI JORIY ETISHNING AHAMIYATI

1. Anderson, R. (2007). The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision

Automation. Oxford: Oxford University Press.

2. Baesens, B., Roesch, D., & Scheule, H. (2016). Credit Risk Analytics: Measurement Techniques, Applications, and

Examples in SAS. Hoboken: John Wiley & Sons.

3. Barocas, S., & Selbst, A. D. (2016). Big Data’s Disparate Impact. California Law Review, 104(3), 671–732.

4. Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022). Consumer-lending discrimination in the FinTech era. Journal

of Financial Economics, 143(1), 30–56.

5. Bellotti, T., & Crook, J. (2009). Support vector machines for credit scoring and discovery of significant features. Expert

Systems with Applications, 36(2), 3302–3308.

6. Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the rise of FinTechs: Credit scoring using digital footprints. The

Review of Financial Studies, 33(7), 2845–2897.

7. Board of Governors of the Federal Reserve System. (2011). Supervisory Guidance on Model Risk Management: SR

11-7. Washington, DC: Federal Reserve System.

8. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk

management. Computational Economics, 57, 203–216.

9. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD

International Conference on Knowledge Discovery and Data Mining (pp. 785–794). New York: ACM.

10. Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big

Data, 5(2), 153–163.

11. Dumitrescu, E., Hué, S., Hurlin, C., & Tokpavi, S. (2022). Machine learning for credit scoring: Improving logistic

regression with non-linear decision-tree effects. European Journal of Operational Research, 297(3), 1178–1192.

12. Durand, D. (1941). Risk Elements in Consumer Instalment Financing. New York: National Bureau of Economic

Research.

13. Ensign, D., Friedler, S. A., Neville, S., Scheidegger, C., & Venkatasubramanian, S. (2018). Runaway feedback loops

in predictive policing. Proceedings of Machine Learning Research, 81, 1–12.

14. European Banking Authority. (2020). Guidelines on Loan Origination and Monitoring. Paris: European Banking

Authority.

15. European Commission. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying

down harmonised rules on artificial intelligence. Official Journal of the European Union.

16. Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine

learning on credit markets. The Journal of Finance, 77(1), 5–47.

17. Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in Neural

Information Processing Systems, 29, 3315–3323.

18. Jagtiani, J., & Lemieux, C. (2019). The roles of alternative data and machine learning in FinTech lending: Evidence

from the LendingClub consumer platform. Financial Management, 48(4), 1009–1029.

19. 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. In Advances in Neural Information Processing Systems, 30, 3146–3154.

20. Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification

algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136.

21. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural

Information Processing Systems, 30, 4765–4774.

22. Luo, C., Wu, D., & Wu, D. (2017). A deep learning approach for credit scoring using credit default swaps. Engineering

Applications of Artificial Intelligence, 65, 465–470.

23. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and metaanalyses:

The PRISMA statement. PLOS Medicine, 6(7), e1000097.

24. Moscatelli, M., Narizzano, S., Parlapiano, F., & Viggiano, G. (2020). Corporate default forecasting with machine

learning. Expert Systems with Applications, 161, 113567.

25. Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with

categorical features. In Advances in Neural Information Processing Systems, 31, 6638–6648.

26. Prudential Regulation Authority. (2023). Model Risk Management Principles for Banks: Supervisory Statement SS1/23.

London: Bank of England.

27. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable

models instead. Nature Machine Intelligence, 1(5), 206–215.

28. Slack, D., Hilgard, S., Jia, E., Singh, S., & Lakkaraju, H. (2020). Fooling LIME and SHAP: Adversarial attacks on post

hoc explanation methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (pp. 180–186).

29. Thomas, L. C., Edelman, D. B., & Crook, J. N. (2002). Credit Scoring and Its Applications. Philadelphia: Society for

Industrial and Applied Mathematics.

30. Wiginton, J. C. (1980). A note on the comparison of logit and discriminant models of consumer credit behavior. Journal

of Financial and Quantitative Analysis, 15(3), 757–770.

31. Wolff, R. F., Moons, K. G. M., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., &

Mallett, S. (2019). PROBAST: A tool to assess the risk of bias and applicability of prediction model studies. Annals of

Internal Medicine, 170(1), 51–58.

32. World Bank Group. (2022). The Use of Alternative Data in Credit Risk Assessment: Opportunities, Risks, and

Challenges. Washington, DC: World Bank Group.

33. Xia, Y., Liu, C., Li, Y. Y., & Liu, N. (2017). A boosted decision tree approach using Bayesian hyper-parameter optimization

for credit scoring. Expert Systems with Applications, 78, 225–241.