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Research Opportunities for Neural Networks: The Case for Credit

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  • Brad S. Trinkle
  • Amelia A. Baldwin

Abstract

This article identifies research opportunities in the use of artificial neural networks in credit scoring and related business intelligence situations, particularly as they have been emerging in the global economy. In the literature review, particular attention is paid to commercial lending credit risk assessment and consumer credit scoring. Investors and auditors need models that can predict whether a customer will stay viable. Lenders must manage their credit risk to maximize profits and cash flow, while minimizing losses. As the global economic recession continues, investors are tightening their investment belts and need models that help them make better investment decisions, while lenders must strengthen lending practices and better identify both good and bad credit risks. Artificial neural networks may help firms improve their credit model development, and thereby their credit decisions and profitability. Such technology may also help improve development in emerging economies. Copyright © 2016 John Wiley & Sons, Ltd.

Suggested Citation

  • Brad S. Trinkle & Amelia A. Baldwin, 2016. "Research Opportunities for Neural Networks: The Case for Credit," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 240-254, July.
    • Handle: RePEc:wly:isacfm:v:23:y:2016:i:3:p:240-254
    DOI: 10.1002/isaf.1394
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    References listed on IDEAS

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    1. Altman, Edward I., 1980. "Commercial Bank Lending: Process, Credit Scoring, and Costs of Errors in Lending," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(4), pages 813-832, November.
    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. Dorota Witkowska, 2006. "Discrete Choice Model Application to the Credit Risk Evaluation," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 12(1), pages 33-42, February.
    4. Dorota Witkowska, 1999. "Applying artificial neural networks to bank-decision simulations," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(3), pages 350-368, August.
    5. Wu, Chunchi & Wang, Xu-Ming, 2000. "A Neural Network Approach for Analyzing Small Business Lending Decisions," Review of Quantitative Finance and Accounting, Springer, vol. 15(3), pages 259-276, November.
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    8. 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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    13. repec:kap:iaecre:v:12:y:2006:i:1:p:33-42 is not listed on IDEAS
    14. Geetesh Bhardwaj & Rajdeep Sengupta, 2011. "Credit scoring and loan default," Working Papers 2011-040, Federal Reserve Bank of St. Louis.
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