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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. 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.
    2. 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.
    3. 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.
    4. Hussein A. Abdou & John Pointon, 2009. "Credit scoring and decision making in Egyptian public sector banks," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 5(4), pages 391-406, September.
    5. Geetesh Bhardwaj & Rajdeep Sengupta, 2011. "Credit scoring and loan default," Working Papers 2011-040, Federal Reserve Bank of St. Louis.
    6. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    7. 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.
    8. 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.
    9. 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.
    10. Bart Baesens & Rudy Setiono & Christophe Mues & Jan Vanthienen, 2003. "Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation," Management Science, INFORMS, vol. 49(3), pages 312-329, March.
    11. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    12. Piramuthu, Selwyn, 1999. "Financial credit-risk evaluation with neural and neurofuzzy systems," European Journal of Operational Research, Elsevier, vol. 112(2), pages 310-321, January.
    13. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    14. Alan Lucas, 2001. "Statistical challenges in credit card issuing," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(1), pages 83-92, January.
    15. repec:kap:iaecre:v:12:y:2006:i:1:p:33-42 is not listed on IDEAS
    16. Setiono, Rudy & Baesens, Bart & Mues, Christophe, 2009. "A note on knowledge discovery using neural networks and its application to credit card screening," European Journal of Operational Research, Elsevier, vol. 192(1), pages 326-332, January.
    17. Sanjeev Mittal & Pankaj Gupta & K. Jain, 2011. "Neural network credit scoring model for micro enterprise financing in India," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 3(3), pages 224-242, October.
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