Artificial Neural Networks with Gradient Learning Algorithm for Credit Scoring

Recently, credit scoring problems have come into prominence depending on growing the number of applicants. As known from literature, the traditional techniques are not sufficient to model this kind of problems accurately. For this reason, the researchers are still struggling to develop the novel techniques and improve the current ones to achieve better solutions. In this paper, credit scoring problem is handled by artificial neural networks (ANNs) because they provide flexible modeling procedure and superior performances in the nonlinear environments. However, the researchers mostly overlook some important requirements such as model complexity, overfitting and selection of optimization algorithm during training of ANNs. This paper presents an efficient procedure that allows estimating more robust credit scoring models by means of the information criteria and the early stopping approach based on the cross-validation technique. In the application section, ANNs are trained by various gradient based algorithms over German credit scoring data, and then their classification performances are compared with each other and logistic regression. According to results, the performance of ANNs is better than logistic regression.

Keywords:

-,

PDF

___

Abdou, H., Pointon, J., and El-Masry, A. (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications, 35, 1275-1292.
Arifovic, J. and Gençay, R. (2001). Using genetic algorithms to select architecture of a feedforward artificial neural network. Physica A, 289, 574-594.
Bazmara, A. and Donighi, S.S. (2014). Bank customer credit scoring by using fuzzy expert system. I.J. Intelligent Systems and Applications, 11, 29-35.
Ben-David, A. and Frank, E. (2009). Accuracy of machine learning models versus “hand crafted” expert systems - A credit scoring case study. Expert Systems with Applications, 36, 5264-5271.
Bishop, C. (2010). Neural networks for pattern recognition, Oxford University Press.
Blanco, A., Delgado, M. and Pegalajar, M.C. (2001). A real- coded genetic algorithm for training recurrent neural networks. Neural Networks, 14, 93-105.
Blanco, A., Pino-Mejias, R., Lara, J. and Rayo, S. (2013). Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Systems with Applications, 40, 356-364.
Bozdogan, H. (2000). Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology, 44(1), 62-91.
Chalkiadakis, I., Rovithakis, G. and Zervakis, M. (2001). A structural genetic algorithm to optimize high order neural network architecture, ESANN’2001 proceedings- European Symposium on Artificial Neural Networks Bruges (Belgium), 185-192.
Desai, V.S., Crook, J.N. and Overstreet, G.A.J. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95, 24-37.
Faraway, J. and Chatfield, C. (1998). Time series forecasting with neural networks: A comparative study using the airline data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(2), 231-250.
Freitas, J.F.G. (2000). Bayesian methods for neural networks, PhD. Thesis, Trinity College University of Cambridge and Cambridge University Engineering Department, UK.
Golden, R.M. (1996). Mathematical methods for neural network analysis and design, The MIT Press, England.
Hamadani, A.Z., Shalbafzadeh, A., Rezvan, T. and Moghadam, A. (2013). An integrated genetic-based model of naive bayes networks for credit scoring. International Journal of Artificial Intelligence & Applications (IJAIA), 4(1).
Hsieh, N.C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications,27(4), 623-633.
Kocadağlı, O. and Aşıkgil, B. (2014). Nonlinear time series forecasting with Bayesian neural networks. Expert Systems with Applications, 41, 6596-6610.
Lee, T.S., Chiu, C.C., Lu, C.J. and Chen, I.F. (2002). Credit scoring using the hybrid neural discriminant technique. Expert Systems with Applications, 23, 245-254.
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), 743-752.
Malhotra, R. and Malhotra, D.K. (2003). Evaluating consumer loans using neural networks. The International Journal of Management Science, 31, 83-96.
Matlab 7.12, http://www.mathworks.com/help/
McQuarrie, A. D. R. and Tsai, C.L. (2007). Regression and time series model selection. World Scientific Publishing Co.
Mirkin, B. (1996). Mathematical classification and clustering. Kluwer Academic Publishers, 74-76.
Moller, M. (1993). A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6(4), 525-533.
Niklis, D., Doumpos, M. and Zopounidis, C. (2014). Combining market and accounting-based models for credit scoring using a classification scheme based on support vector machines. Applied Mathematics and Computation, 234, 69-81.
Nocedal J. and Wright S. J. (2006). Numerical Optimization, 2nd Edition, Springer.
Ong, C., Huang, J. and Tzeng, G. (2005). Building credit scoring models using genetic programming. Expert Systems with Applications, 29(1), 41-47.
Oreski, S., Oreski, D. and Oreski, G. (2012). Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment. Expert Systems with Applications, 39, 12605-12617.
Schebesch, K.B. and Stecking, R. (2005). Support vector machines for classifying and describing credit applicants: Detecting typical and critical regions. Journal of the Operational Research Society, 56, 1082-1088.
Seiffert, U. (2001). Multiple layer perceptron training using genetic algorithms, ESANN’2001 proceedings-European Symposium on Artificial Neural Networks Bruges (Belgium),159-164.
Setiono, R., Baesens, B. and Martens, D. (2012). Rule extraction from neural networks and support vector machines for credit scoring, Data Mining: Foundations and Intelligent Paradigms Intelligent Systems Reference Library, Springer, Book Chapter, 25, 299-320.
Silva, L. M., Marques de Sá, J. and Alexandre, L. A. (2008). Data classification with multilayer perceptrons using a generalized error function. Neural Networks, (21) 1302 – 1310.
Šušteršič, M., Mramor, D., and Zupan, J. (2009). Consumer credit scoring models with limited data. Expert Systems with Applications, 36, 4736-4744.
Thomas, L.C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to customers. International Journal of Forecasting, 16(2), 149-172.
http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit +Data). UCI, Machine Learning Repository.
Wang, G., Hao, J., Ma, J. and Jiang, H. (2011). A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications, 38, 223-230.
West, D. (2000). Neural network credit scoring models. Computers & Operations Research, 27, 1131-1152.