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A fuzzy credit-rating approach for commercial loans: a Taiwan case

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  • Chen, Liang-Hsuan
  • Chiou, Tai-Wei

Abstract

Credit rating for commercial loans is an important task for loan officers of a bank who usually use a credit-rating table based on a point system. However, the employment of such a table may neglect the fuzzy nature of credit-rating processes. This paper presents a fuzzy credit-rating approach to deal with the problem arisen from the credit-rating table currently used in Taiwan. First, the evaluation criteria are modeled as a hierarchical decision structure. An evidence fusion technique, namely the fuzzy integral, is then employed for aggregating credit information in a bottom-up way. This technique not only considers the objective evidence but also the relative importance of each criterion. Furthermore, the proposed approach uses fuzzy sets (fuzzy numbers) to describe the criteria, so that the final credit-rating results can reveal changes of credit information. The membership degrees of the five rating levels for describing the final evaluation results can provide loan officers with more valuable information for making decisions. A numerical example is used to demonstrate the applicability of the approach.

Suggested Citation

  • Chen, Liang-Hsuan & Chiou, Tai-Wei, 1999. "A fuzzy credit-rating approach for commercial loans: a Taiwan case," Omega, Elsevier, vol. 27(4), pages 407-419, August.
    • Handle: RePEc:eee:jomega:v:27:y:1999:i:4:p:407-419
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    1. Srinivasan, Venkat & Kim, Yong H, 1987. "Credit Granting: A Comparative Analysis of Classification Procedures," Journal of Finance, American Finance Association, vol. 42(3), pages 665-681, July.
    2. West, Robert Craig, 1985. "A factor-analytic approach to bank condition," Journal of Banking & Finance, Elsevier, vol. 9(2), pages 253-266, June.
    3. Levy, J & Mallach, E & Duchessi, P, 1991. "A fuzzy logic evaluation system for commercial loan analysis," Omega, Elsevier, vol. 19(6), pages 651-669.
    4. Collins, Robert A. & Green, Richard D., 1982. "Statistical methods for bankruptcy forecasting," Journal of Economics and Business, Elsevier, vol. 34(4), pages 349-354.
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    Cited by:

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