IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v27y1999i4p407-419.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305-0483(98)00051-6
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Levy, J & Mallach, E & Duchessi, P, 1991. "A fuzzy logic evaluation system for commercial loan analysis," Omega, Elsevier, vol. 19(6), pages 651-669.
    2. 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.
    3. West, Robert Craig, 1985. "A factor-analytic approach to bank condition," Journal of Banking & Finance, Elsevier, vol. 9(2), pages 253-266, June.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haider A. Khan, 2002. "Can Banks Learn to Be Rational?," CIRJE F-Series CIRJE-F-151, CIRJE, Faculty of Economics, University of Tokyo.
    2. Feng, Cheng-Min & Wu, Pei-Ju & Chia, Kai-Chieh, 2010. "A hybrid fuzzy integral decision-making model for locating manufacturing centers in China: A case study," European Journal of Operational Research, Elsevier, vol. 200(1), pages 63-73, January.
    3. Haider A. Khan, 2004. "General Conclusions: From Crisis to a Global Political Economy of Freedom," Palgrave Macmillan Books, in: Global Markets and Financial Crises in Asia, chapter 9, pages 193-211, Palgrave Macmillan.
    4. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    5. David Opresnik & Maurizio Fiasché & Marco Taisch & Manuel Hirsch, 0. "An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy," Information Technology and Management, Springer, vol. 0, pages 1-17.
    6. Carlo Alberto Magni & Stefano Malagoli & Andrea Marchioni & Giovanni Mastroleo, 2020. "Rating firms and sensitivity analysis," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(12), pages 1940-1958, December.
    7. Kao, Chiang & Liu, Shiang-Tai, 2003. "A mathematical programming approach to fuzzy efficiency ranking," International Journal of Production Economics, Elsevier, vol. 86(2), pages 145-154, November.
    8. Xinping Wang & Cheng Zhang & Jun Deng & Chang Su & Zhenzhe Gao, 2022. "Analysis of Factors Influencing Miners’ Unsafe Behaviors in Intelligent Mines using a Novel Hybrid MCDM Model," IJERPH, MDPI, vol. 19(12), pages 1-30, June.
    9. Zhixin Liu & Ping He & Bo Chen, 2019. "A Markov decision model for consumer term-loan collections," Review of Quantitative Finance and Accounting, Springer, vol. 52(4), pages 1043-1064, May.
    10. Beynon, Malcolm J. & Peel, Michael J. & Tang, Yu-Cheng, 2004. "The application of fuzzy decision tree analysis in an exposition of the antecedents of audit fees," Omega, Elsevier, vol. 32(3), pages 231-244, June.
    11. Dursun, Mehtap & Karsak, E. Ertugrul & Karadayi, Melis Almula, 2011. "Assessment of health-care waste treatment alternatives using fuzzy multi-criteria decision making approaches," Resources, Conservation & Recycling, Elsevier, vol. 57(C), pages 98-107.
    12. Oralhan Burcu & Kirdök Nur & Oralhan Zeki, 2022. "Evaluation of Ski Centers’ Performance Using Multiple-Criteria Decision-Making Methods," Polish Journal of Sport and Tourism, Sciendo, vol. 29(3), pages 29-35, September.
    13. Ismail Hakki GOKGOZ & Fatih ALTINEL & F.Pinar Yetkin GOKGOZ & Ilker KOC, 2013. "Classification of Turkish Commercial Banks Under Fuzzy c-Means Clustering," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, vol. 7(2), pages 13-36.
    14. Ozer, Muammer, 2001. "User segmentation of online music services using fuzzy clustering," Omega, Elsevier, vol. 29(2), pages 193-206, April.
    15. David Opresnik & Maurizio Fiasché & Marco Taisch & Manuel Hirsch, 2017. "An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy," Information Technology and Management, Springer, vol. 18(2), pages 131-147, June.
    16. Jan-Henning Trustorff & Paul Konrad & Jens Leker, 2011. "Credit risk prediction using support vector machines," Review of Quantitative Finance and Accounting, Springer, vol. 36(4), pages 565-581, May.
    17. Pranith K. Roy & Krishnendu Shaw, 2023. "A credit scoring model for SMEs using AHP and TOPSIS," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 372-391, January.
    18. Doumpos, Michalis & Figueira, José Rui, 2019. "A multicriteria outranking approach for modeling corporate credit ratings: An application of the Electre Tri-nC method," Omega, Elsevier, vol. 82(C), pages 166-180.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haider A. Khan, 2002. "Can Banks Learn to Be Rational?," CIRJE F-Series CIRJE-F-151, CIRJE, Faculty of Economics, University of Tokyo.
    2. Haider A. Khan, 2004. "General Conclusions: From Crisis to a Global Political Economy of Freedom," Palgrave Macmillan Books, in: Global Markets and Financial Crises in Asia, chapter 9, pages 193-211, Palgrave Macmillan.
    3. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    4. Moshe Leshno & Yishay Spector, 1997. "The effect of training data set size and the complexity of the separation function on neural network classification capability: The two‐group case," Naval Research Logistics (NRL), John Wiley & Sons, vol. 44(8), pages 699-717, December.
    5. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    6. Nabeel Al-Milli & Amjad Hudaib & Nadim Obeid, 2021. "Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem," Mathematics, MDPI, vol. 9(8), pages 1-18, April.
    7. Philip Swicegood & Jeffrey A. Clark, 2001. "Off‐site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(3), pages 169-186, September.
    8. Guo, Lin, 1999. "When and why did FSLIC resolve insolvent thrifts?," Journal of Banking & Finance, Elsevier, vol. 23(6), pages 955-990, June.
    9. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    10. Carlos Serrano-Cinca & Yolanda Fuertes-Call鮠 & Bego uti鲲ez-Nieto & Beatriz Cuellar-Fernᮤez, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    11. Nijskens, Rob & Mokas, Dimitris, 2019. "Credit Risk in Commercial Real Estate Bank Loans : The Role of Idiosyncratic versus Macro-Economic Factors," Other publications TiSEM ea4f2f0e-dc50-4987-91d3-6, Tilburg University, School of Economics and Management.
    12. Premachandra, I.M. & Chen, Yao & Watson, John, 2011. "DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment," Omega, Elsevier, vol. 39(6), pages 620-626, December.
    13. Kaylen, Michael S. & Devino, Gary T. & Procter, Michael H., 1988. "Optimal Use Of Qualitative Models: An Application To Country Grain Elevator Bankruptcies," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 20(2), pages 1-7, December.
    14. Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    15. Golaszewski, Richard & Sanders, Matthew, 1991. "Financial Stress in the U.S. Airline Industry," Transportation Research Forum Proceedings 1990s 319097, Transportation Research Forum.
    16. Thomas B. King & Daniel A. Nuxoll & Timothy J. Yeager, 2006. "Are the causes of bank distress changing? can researchers keep up?," Review, Federal Reserve Bank of St. Louis, vol. 88(Jan), pages 57-80.
    17. Halil Erdal & Aykut Ekinci, 2013. "A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 199-215, August.
    18. Vincent Bouvatier & Michael Brei & Xi Yang, 2014. "Bank Failures and the Source of Strength Doctrine," Working Papers hal-04141351, HAL.
    19. repec:zbw:bofrdp:2009_035 is not listed on IDEAS
    20. James B. Thomson, 1991. "Predicting bank failures in the 1980s," Economic Review, Federal Reserve Bank of Cleveland, vol. 27(Q I), pages 9-20.
    21. Demyanyk, Yuliya & Hasan, Iftekhar, 2009. "Financial crises and bank failures: a review of prediction methods," Bank of Finland Research Discussion Papers 35/2009, Bank of Finland.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:27:y:1999:i:4:p:407-419. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.