[1]
Adams, J., Hayunga, D., Mansi, S., Reeb, D., & Verardi, V. (2019). Identifying And Treating Outliers In Finance. Financial Management, 48(2), 345–384. Https://Doi.Org/10.1111/Fima.12269
[2]
Addy, W. A., Ugochukwu, C. E., Oyewole, A. T., Ofodile, O. C., Adeoye, O. B., & Okoye, C. C. (2024). Predictive Analytics In Credit Risk Management For Banks: A Comprehensive Review. Gsc Advanced Research And Reviews, 18(2), 434–449. Https://Doi.Org/10.30574/Gscarr.2024.18.2.0077
[3]
Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Ser, J. D., Díaz-Rodríguez, N., & Herrera, F. (2023). Explainable Artificial Intelligence (Xai): What We Know And What Is Left To Attain Trustworthy Artificial Intelligence. Information Fusion, 99(101805), 101805. Sciencedirect.
Https://Doi.Org/10.1016/J.Inffus.2023.101805
[4]
Amani, F. A., & Fadlalla, A. M. (2017). Data Mining Applications In Accounting: A Review Of The Literature And Organizing Framework. International Journal Of Accounting Information Systems, 24, 32–58. Https://Doi.Org/10.1016/J.Accinf.2016.12.004
[5]
Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data Mining For Credit Card Fraud: A Comparative Study. Decision Support Systems, 50(3), 602–613. Https://Doi.Org/10.1016/J.Dss.2010.08.008.
