Development of a quick credibility scoring decision support system using fuzzy TOPSIS

In this study, a quick credibility scoring decision support system is developed for the banks to determine the credibility of manufacturing firms in Turkey. The proposed decision support system is expected to be used by the banks when they want to determine whether an applicant firm is worth a detailed credit check or not. Using such a quick credit scoring decision model reduces the banks' workload. The proposed credit scoring model is based on the financial ratios and fuzzy TOPSIS approach. It obtains two separate scores which reflect the attractiveness of manufacturing industries within the overall economy and manufacturing firms' performance with respect to its competitors belonging to the same industry. These two scores are then used to determine the credibility of applicant manufacturing firms. The developed decision support system is tested with various real cases and satisfactory results are obtained. An application is also provided in the paper for illustrative purposes.

[1]  M. Yurdakul,et al.  AHP approach in the credit evaluation of the manufacturing firms in Turkey , 2004 .

[2]  Andrea Rangone,et al.  A reference framework for the application of MADM fuzzy techniques to selecting AMTS , 1998 .

[3]  Ray Tsaih,et al.  Credit scoring system for small business loans , 2004, Decis. Support Syst..

[4]  C. Zopounidis,et al.  Assessing financial risks using a multicriteria sorting procedure: the case of country risk assessment , 2001 .

[5]  Ahmet Burak Emel,et al.  A credit scoring approach for the commercial banking sector , 2003 .

[6]  A. Saunders,et al.  Credit risk measurement: Developments over the last 20 years , 1997 .

[7]  Celik Parkan,et al.  Decision-making and performance measurement models with applications to robot selection , 1999 .

[8]  H. S. Byun,et al.  A decision support system for the selection of a rapid prototyping process using the modified TOPSIS method , 2005 .

[9]  Jian-Bo Yang,et al.  Multiple Criteria Decision Support in Engineering Design , 1998 .

[10]  Tai-Yue Wang,et al.  Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision-making approach , 2000 .

[11]  Bernard C. Jiang,et al.  Development of a fuzzy decision model for manufacturability evaluation , 2003, J. Intell. Manuf..

[12]  M. Yurdakul *,et al.  Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches , 2005 .

[13]  E. Altman,et al.  ZETATM analysis A new model to identify bankruptcy risk of corporations , 1977 .

[14]  Young-Chan Lee,et al.  A practical approach to credit scoring , 2008, Expert Syst. Appl..

[15]  Chen-Tung Chen,et al.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000, Fuzzy Sets Syst..

[16]  Ching-Hsue Cheng,et al.  Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation , 2002, Eur. J. Oper. Res..

[17]  Chih-Fong Tsai,et al.  Using neural network ensembles for bankruptcy prediction and credit scoring , 2008, Expert Syst. Appl..

[18]  T. Chu,et al.  A Fuzzy TOPSIS Method for Robot Selection , 2003 .

[19]  C ONG,et al.  Building credit scoring models using genetic programming , 2005, Expert Syst. Appl..

[20]  Mu-Chen Chen,et al.  Credit scoring and rejected instances reassigning through evolutionary computation techniques , 2003, Expert Syst. Appl..

[21]  Joe Zhu,et al.  Multi-factor performance measure model with an application to Fortune 500 companies , 2000, Eur. J. Oper. Res..