Credit Risk Prediction Using Artificial Neural Network Algorithm

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Deepak Kumar Gupta 1,* Shruti Goyal 1

1. UCD Michael Smurfit Graduate Business School, Dublin, Ireland

* Corresponding author.


Received: 10 Mar. 2018 / Revised: 26 Mar. 2018 / Accepted: 10 Apr. 2018 / Published: 8 May 2018

Index Terms

Credit Risk, Artificial Neural Network, Linear Regression, Credit Risk Analysis, Credit Rating


Artificial neural network is an information processing system which is influenced by the human brain and works on the same principles of the biological nervous system. They possess the ability to extract meaning from complex and intricate data, by detecting trends and extracting patterns from it. This paper illustrates the ability of neural network model and linear regression model constructed to predict the creditworthiness of an application accurately and precisely with minimal false predictions and errors. The results are shown to be similar for both the models, thus, models are efficient to use depending on the type of application and attributes.

Cite This Paper

Deepak Kumar Gupta, Shruti Goyal, "Credit Risk Prediction Using Artificial Neural Network Algorithm", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.5, pp. 9-16, 2018. DOI:10.5815/ijmecs.2018.05.02


[1]Bielecki, T.R. and Rutkowski, M., 2013. Credit risk: modeling, valuation and hedging. Springer Science & Business Media.
[2]Imbierowicz, B. and Rauch, C., 2014. The relationship between liquidity risk and credit risk in banks. Journal of Banking & Finance, 40, pp.242-256.
[3]Acharya, V., Davydenko, S.A. and Strebulaev, I.A., 2012. Cash holdings and credit risk. The Review of Financial Studies, 25(12), pp.3572-3609.
[4]Cole, S., Kanz, M. and Klapper, L., 2015. Incentivizing calculated riskÔÇÉtaking: evidence from an experiment with commercial bank loan officers. The Journal of Finance, 70(2), pp.537-575.
[5]Pacelli, V. and Azzollini, M., 2011. An artificial neural network approach for credit risk management. Journal of Intelligent Learning Systems and Applications, 3(02), p.103.
[6]Kozeny, V., 2015. Genetic algorithms for credit scoring: Alternative fitness function performance comparison. Expert Systems with applications, 42(6), pp.2998-3004.
[7]Angelini, E., di Tollo, G. and Roli, A., 2008. A neural network approach for credit risk evaluation. The quarterly review of economics and finance, 48(4), pp.733-755.
[8]Laeven, M.L., Ratnovski, L. and Tong, H., 2014. Bank size and systemic risk (No. 14). International Monetary Fund.
[9]Zopounidis, C. and Doumpos, M., 2002. Multicriteria classification and sorting methods: a literature review. European Journal of Operational Research, 138(2), pp.229-246.
[10]Eisenbeis, R.A., 1977. Pitfalls in the application of discriminant analysis in business, finance, and economics. The Journal of Finance, 32(3), pp.875-900.
[11]Khemakhem, S. and Boujelbene, Y., 2015. Credit risk prediction: A comparative study between discriminant analysis and the neural network approach. Accounting and Management Information Systems, 14(1), p.60.
[12]Lessmann, S., Baesens, B., Seow, H.V. and 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), pp.124-136.
[13]Thomas, L., Crook, J. and Edelman, D., 2017. Credit scoring and its applications (Vol. 2). Siam.
[14]Halper, S.C., Wilson, C.A. and Hourigan, S.M., Interthinx Inc, 2013. Automated loan risk assessment system and method. U.S. Patent 8,458,082.
[15]Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I. and de Mendonça, A., 2011. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC research notes, 4(1), p.299.
[16]Hotz, S., Kelly, J., Srinivasan, K. and Jindia, A.K., Compucredit Intellectual Property Holdings Corp II, 2011. Method and system for rapid loan approval. U.S. Patent 7,933,833.
[17]Mohammadi, N. and Zangeneh, M., 2016. Customer Credit Risk Assessment using Artificial Neural Networks. IJ Information Technology and Computer Science, 8(3), pp.58-66.
[18]Brown, K. and Moles, P., 2014. Credit risk management. K. Brown & P. Moles, Credit Risk Management, p.16.
[19]Mester, L.J., 1997. What’s the point of credit scoring? Business review, 3(Sep/Oct), pp.3-16.
[20]Bornhofen, B., Byrne, L., Bray, D., Elder, R., Feight, R., Pinnola, K.H., Shimshi, F., Quinlan, R. and Cheeseman, M., Citibank NA, 2018. Method and system for the issuance of instant credit. U.S. Patent 9,898,780.
[21]Halvaiee, N.S. and Akbari, M.K., 2014. A novel model for credit card fraud detection using Artificial Immune Systems. Applied Soft Computing, 24, pp.40-49.
[22]Bunn, D. and Wright, G., 1991. Interaction of judgemental and statistical forecasting methods: issues & analysis. Management science, 37(5), pp.501-518.
[23]Bazmara, A. and Donighi, S.S., 2014. Bank customer credit scoring by using fuzzy expert system. International Journal of Intelligent Systems and Applications, 6(11), p.29.
[24]Rahman, M.M., Ahmed, S. and Shuvo, M.H., 2014. Nearest Neighbor Classifier Method for Making Loan Decision in Commercial Bank. International Journal of Intelligent Systems and Applications, 6(8), p.60.
[25]Lee, T.S. and Chen, I.F., 2005. A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications, 28(4), pp.743-752.
[26]Harris, T., 2015. Credit scoring using the clustered support vector machine. Expert Systems with Applications, 42(2), pp.741-750.
[27]Hosmer, D.W., Jovanovic, B. and Lemeshow, S., 1989. Best subsets logistic regression. Biometrics, pp.1265-1270.
[28]Altland, H.W., 1999. Regression analysis: statistical modeling of a response variable.
[29]Chittineni, S. and Bhogapathi, R.B., 2012. Determining contribution of features in clustering multidimensional data using neural network. IJ Information Technology and Computer Science, 10, pp.29-36.
[30]Ghosh, S. and Reilly, D.L., 1994, January. Credit card fraud detection with a neural-network. In System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on (Vol. 3, pp. 621-630). IEEE.
[31]Laitinen, E.K., 1999. Predicting a corporate credit analyst's risk estimate by logistic and linear models. International review of financial analysis, 8(2), pp.97-121.
[32]Pang, S.L., Wang, Y.M. and Bai, Y.H., 2002, November. Credit scoring model based on neural network. In Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on (Vol. 4, pp. 1742-1746). IEEE.
[33]Atiya, A.F., 2001. Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on neural networks, 12(4), pp.929-935.
[34]Hornik, K., Stinchcombe, M. and White, H., 1989. Multilayer feedforward networks are universal approximators. Neural networks, 2(5), pp.359-366.
[35]Pineda, F.J., 1987. Generalization of back-propagation to recurrent neural networks. Physical review letters, 59(19), p.2229.
[36]Hand, D.J. and Henley, W.E., 1997. Statistical classification methods in consumer credit scoring: a review. Journal of the Royal Statistical Society: Series A (Statistics in Society), 160(3), pp.523-541.