References
Afriyie, H.O & Akotey, J.O (2012). Credit risk management and profitability of selected rural banks in Ghana. African Development Workshop.https://tinyurl.com/j8b622ec Ahmad, G., & Ashkan, A. (2011). Application of artificial intelligence techniques for credit risk evaluation. International Journal of Modeling and Optimization, 1(3), 243-249. Bakpo, F. S., & Kabari, L. G. (2009). Credit risk evaluation system: An Artificial neural. network approach. Nigerian Journal of Technology, 28 (1). 36 – 44. https://tinyurl.com/2p87etua Bradley, A.P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159. Cheng, J. & Greiner, R. (2013). Comparing Bayesian Network Classifiers. Retrieved on 5th August, 2024 from https://arxiv.org/pdf/1301.6684 Deepanshu, B. (2019), A complete guide to Credit risk modelling. Retrieved on 6th August, 2024 from https://www.listendata.com/2019/08/credit-risk-modelling.html. Diab, D. M., & El Hindi, K. M. (2017). Using differential evolution for fine tuning naïve Bayesian classifiers and its application for text classification. Applied Soft Computing Journal, 54, 183– https://doi.org/10.1016/j.asoc.2016.12.043 Eriki, P.O & Udegbunam, R. (2013, October 14). Predicting corporate distress in the Nigerian stock market: Neural network versus multiple discriminant analysis. African Journal of Business Management, 7(38), 3856-3863. https://doi.org/ 10.5897/AJBM09.152 Ezenkwu, C.P., Udofia K. M. &. Sihitshuwam, F.D. (2016). Development of a credit risk evaluation system using multilayer feedforward neural networks. African Journal of Computing & ICT,8(1). 165https://www.researchgate.net/publication/311650187_Development_of_a_Credit_Risk_E valuation_System_Using_Multilayer_Feedforward_Neural_Networks Gandhi, R. (2018). Naive Bayes Classifier. Towards Data Science Blog. https://towardsdatascience.com/naive-bayes-classifier-81d512f50a7c Ghatge, A.R., & Halkarnikar. (2015) Estimation of Credit Risk for Business Firms of Nationalized Bank by Neural Network Approach. International Journal of Electronics and Computer Science Engineering, 2(3). 828 – 834. https://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.434.28&rep=rep1&type=pdf Hamadi, M. & Aida, A. (2011). Credit-risk evaluation of a Tunisian commercial bank: Logistic regression vs neural network modelling. International Journal of Accounting & Information Management, 19(2), 92 – 119. https://doi.org/ijaim.2011.36619baa.005 John, G.H., & Langley, P. (1995). Estimating continuous distributions in Bayesian classifiers.Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence,338–345. http://citeseerx.ist.psu.edu/ viewdoc/versions?doi=10.1.1.8.3257. Khemakhem, S., & Boujelbene, Y. (2017). Artificial Intelligence for credit risk assessment: Artificial Neural Network and Support Vector Machines. ACRN Oxford Journal of Finance and Risk Perspectives, 6(2), 1-17. https://tinyurl.com/ycxsbehr Mohamed, W. T., & Boujelbene, Y. (2017). Bank credit risk: Evidence from Tunisia using Bayesian networks. Journal of Accounting, Finance and Auditing Studies, 3/3 (2017) 93- https://tinyurl.com/5tdv2nft Mohammadi, N. & Zangeneh, M. (2016). Customer credit risk assessment using artificial neural networks. International Journal of Information Technology and Computer Science, 8(3), 58-66. https://doi.org/10.5815/ijitcs.2016.03.07. Okesola, O. J., Okokpujie, K. O., Adewale, A. A., John, S. N., & Omoruyi, O. (2017). An improved bank credit scoring model: A naïve bayesian approach. 2017 International Conference on Computational Science and Computational Intelligence (CSCI), 228-233. Pacelli, V. & Azzollini, M. (2011). An artificial neural network approach for credit risk management. Journal of Intelligent Learning Systems and Applications, 3(02), 103-112. http://doi.org/10.4236/jilsa.2011.32012 Ramazan, E., & Gulden, P. (2019). The effect of credit risk on financial performance of deposit banks in Turkey. Procedia Computer Science, 158 (2019), 979–987. http://doi.org/10.1016/j.procs.2019.09.139 Siddharth, M.,Hao, L., & Jiabo, H. (2020),Machine Learning for Subsurface Characterization. Gulf Professional Publishing. https://doi.org/10.1016/C2018-0-01926-X Taiwo, J.N., Ucheaga, E.G., Achugamonu, B.U., Adetiloye, K., Okoye, L. &, Agwu, M.E. (2017). Credit risk management: Implications on bank performance and lending growth. Saudi Journal of Business and Management Studies,2(5B), 584 –https://tinyurl.com/ye23ujxj Teles, G. Rodrigues, J., Rabelo, R. A. L., & Kozlov, S. (2020). Artificial neural network and Bayesian network models for credit risk prediction. Journal of Artificial Intelligence and Systems, 2. 118-132. https://doi.org/10.33969/AIS.2020.21008. Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, Ng, A., Liu, B., Yu, P.S., Zhou,Z., Steinbach, M., Hand, D.J., & Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems(Vol.14),1-37.https://doi.org/10.1007/s10115-007-0114-2 Yimka, A. T., Abimbola, C., & Adekunle, O. (2015, Feb 2015). Credit risk management and financial performance of selected commercial banks in Nigeria. Journal of Economic & Financial Studies, 3(01), 01- 09. https://doi.org/10.18533/jefs.v3i01.73