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Evaluating consumer loans using neural networks

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  • Malhotra, Rashmi
  • Malhotra, D. K.
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    Abstract

    A number of credit-scoring models that accurately classify consumer loan applications have been developed to aid traditional judgmental methods. This study compares the performance of multiple discriminant analysis (MDA) and neural networks in identifying potential loan. The neural network models consistently perform better than the MDA models in identifying potential problem loans. To alleviate the problem of bias in the training set and to examine the robustness of neural network classifiers in identifying problem loans, we cross-validate our results through seven different samples of the data.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 31 (2003)
    Issue (Month): 2 (April)
    Pages: 83-96

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    Handle: RePEc:eee:jomega:v:31:y:2003:i:2:p:83-96

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    Keywords: Neural networks Credit risk Credit unions;

    References

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    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Desai, Vijay S. & Crook, Jonathan N. & Overstreet, George A., 1996. "A comparison of neural networks and linear scoring models in the credit union environment," European Journal of Operational Research, Elsevier, vol. 95(1), pages 24-37, November.
    3. Philip Hans Franses & Paul van Homelen, 1998. "On forecasting exchange rates using neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(6), pages 589-596.
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    5. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. " A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-89, July.
    6. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    7. Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
    8. Indro, D. C. & Jiang, C. X. & Patuwo, B. E. & Zhang, G. P., 1999. "Predicting mutual fund performance using artificial neural networks," Omega, Elsevier, vol. 27(3), pages 373-380, June.
    9. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
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    Cited by:
    1. Owen P. Hall Jr. & Darrol J. Stanley, 2012. "A comparative modelling analysis of firm performance," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 4(1), pages 43-56.
    2. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring," European Journal of Operational Research, Elsevier, vol. 195(3), pages 942-959, June.
    3. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    4. Ioannidis, Christos & Pasiouras, Fotios & Zopounidis, Constantin, 2010. "Assessing bank soundness with classification techniques," Omega, Elsevier, vol. 38(5), pages 345-357, October.
    5. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.

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