Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Prediction of Credit Default Risk in Financial Institutions Using Artificial Neural Network

Anyanwu Onyekachi Julian and Amanze Bethran Chibuike

Abstract

This research study, examined the Prediction of Credit Default Risk in Financial Institutions using Artificial Neural Network. Credit default risk has remained one of the important fundamental and critical issues widely studied in the financial institutions in Nigeria. Credit Default risk comes into play when a loan borrower fails to repay his loan within the agreed financial contract. Banks and other financial institutions depend heavily on statistical and machine learning method in predicting loan default to the potential losses of granted loans. These machine learning applications cannot achieve full potential prediction without the semantic context in the data. Neural Network initiate the behavior of the human brain to solve both linear and non-linear statistical problems. The study observe that credit risk is the greatest and leading risk in the banking sector as its effects have crippled several financial institutions and have led to the failure of many. Therefore, the study proposed the adoption of Neural Network in predicting credit default risk to improve the prediction model’s accuracy and interoperability. Agile methodology, structured system analysis design methodology (SSADM) was used in the software development to get the total records of credit defaulters. This study designed a system for prediction of Credit Default Risk using Artificial Neural Network. The system is an efficient, accurate and reliable predictive tool which can be employed by financial institutions and lender organizations to solve and manage problem of credit default risk. The programming language PHP Script will be use for the software and My Structured Query Language (MySQL) for database.

Keywords

Artificial Neural NetworkCredit Default RiskPrediction modelStatistical

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

More Articles from INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY

Advances in Algorithmic Contract Scoring for Pre-Negotiation Yield Optimization and Risk Retention

Author: Ngozi Samuel Uzougbo, Michael Ominyi, Cyril Chimelie Anichukwueze, Blessing, Chika Jones

DevTest flow: Designing a Scalable Continuous Testing Pipeline for High-Velocity Software Delivery

Author: Lawal Ahmed Oladimeji, Achori Busayo, Akeju BusayoZainab, Saka Samson, Damilare, Mbah Demian Chidi, Runsewe Similoluwa Mayowa, Oladiti Luqman, Abiodun