Adaptive DEA for clustering of credit clients


Department of Industrial Engineering, Yazd University, Yazd, Iran


Competition among the industrial and service organizations to provide their clients with financial and credit requirements through the banking facilities has considerably increased. On the other hand, the challenge facing these financial and credit resources is that they are limited. Therefore, the optimal allocation of these limited financial resources with the aim of maximizing the investment value is of a great priority for banks and other financial institutes. In this study, first the credit criteria for the applicants for bank facilities have been identified and then based on the improved Data Envelopment Analysis (DEA) technique, an effective method has been proposed for the client clustering. The improved DEA method which is called Golden DEA reduces the calculation time and increases the decision-making operations that ultimately lead to the improvement of the existing method. Also, the improved DEA model provides a short, dynamic and straight path in order to achieve greater efficiency for every institution. The priority provided by the improved DEA method has been compatible with the priority given by the existing DEA method for all of the understudied cases.


[1] ZChe.H., Wang H.S., Chuang C.L., (2010). A fuzzy AHP and DEA approach for making bank loan decisions for small and medium enterprises in Taiwan, Expert Systems with Applications, 37(10), 7189-7199.

[2] Grunert J., Norden L., Weber M., (2005). The role of non-financial factors in internal credit ratings. Journal of Banking & Finance, 29(2), 509-531.

[3] Chi B.W., Hs C.C., A, (2012). Hybrid approach to integrate genetic algorithm into dual scoring model in enhancing the performance of credit scoring model, Expert Systems with Applications, 39(3), 2650-2661.

[4] Abdou. H.A., (2009). Genetic programming for credit scoring: The case of Egyptian public sector banks, Expert Systems with Applications, 36(9), 2009, 11402-11417.

[5] Yap B. W., Ong S.H., Mohamed Husain N.H., (2011). Using data mining to improve assessment of credit worthiness via credit scoring models. Expert Systems with Applications, 38,(10), 13274-13283.

[6] Abdou H., Pointon J., El-Masry A., (2008). Neural nets versus conventional techniques in credit scoring in Egyptian banking. Expert Systems with Applications, 35(3), 1275-1292.

[7] Feroz, E.H. kim, S. and Raab, R.L., (2003). Financial statement analysis: A data envelopment analysis approach, Journal of the OR society, 24, 48-58.

[8] Tsolas I.E., (2004), Modeling bank branch profitability and effectiveness by means of DEA, International Journal of Productivity and Performance Management, 59(5), 432-451.

[9] France, K., (2003). Credit scoring process from a knowledge management prospective. Budapest University of Technology and Economics.

[10]Christoph, J.,(2004). Express credit and bank default risk an application of default prediction models to banks from emerging market economics. International conference on emerging market and global risk management, University of Westminster, London, UK.

[11]Levy,J., Mallach, E., & Duchessi , P.,(1991). A fuzzy logic evaluation system for commercial loan analysis, Omega, 19(6), 651-669.

[12]Marrison, Chris (2002). The Fundamentals of Risk Measurement. New York, New York: McGraw Hill. pp. 340–342. ISBN 0-07-138627-0.

[13]Lee, T., Chiu, C. and Lu, C., (2002). Credit scoring using the hybrid neural discriminate technique. Expert System with Application, 23, 245-254.

[14]Aitman, E.I, (1998). Financial ratio discriminate analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.

[15]Servigny, A.D. and Renault, O., (2004). Measuring and managing credit risk, MC Graw-Hill.

[16]Lyn, C.T., (2000). A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers. International Journal of forecasting, 6, 149-172.

[17]Dellin, H. and Schreviner, M., (2005). Credit scoring, bank, and microfinance: balancing High-tech with High Touch. Woman's world Banking and Microfinance risk management, USA, New York.

[18]Cooper, W.W., Park, K.S. and Yu, G., (1999). IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Management science, 45(4),597-607.

[19]Chiang, Y.H., Chng, E.W.L. and Tang, B.S., (2006). Examining repercussions of consumption and input placed on the construction sector by use of I-O tables and DEA. Building and Environment, 41(4),1-11. [20]Franchon, p. (2003). Variable selection for dynamic measures of efficiency in the computer industry. International Advances in Economic Research (IAER), 9(3), 175-186.

[21]Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

[22]Emel A. B, Oral M., Reisman A., Yolalan R., (2003). A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences, 37(2), 103-123.

[23]Jiao Y., Syau Y.R., Lee E. S., (2007). Modeling credit rating by fuzzy adaptive network. Mathematical and Computer Modelling, 45(5–6),717-731.

[24]Kim, K.J., Ahn H.,(2012). A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach. Computers & Operations Research, 39(8), 1800-1811.

[25]Oreski S., Oreski D., Oreski G., (2012). Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment. Expert System with Application, 39(16), 12605-12617.

[26]Maher, J. and Tarun, K., (1997). Predicting bond ratings using neural networks: A comparison with logistic regression intelligent system in Accounting. Finance and Management, 6, 59-72.

[27]Yardakall, M., and Yusaf, T., (2004). AHP approach in the credit evaluation of the manufacturing in turkey, Intelligent Journal Production Economics, 88, 269-289.

[28]Biener C., Eling M.(2012). Organization and efficiency in the international insurance industry: A crossfrontier analysis. European Journal of Operational Research, 221(2), 454-468.

[29]Halkos, G.E., and Dimitrios, S.S., (2004). Efficiency measurement of the Greek commercial banks with the use of financial ratios: A data envelopment analysis approach. Management Accounting Research, 15, 201- 224.

[30]Mok, V., Godfrey, Y., Zhoozbou, H., and Li, Z., (2007). Leverage technical efficiency and profitability: an application of DEA to foreign-invested toy manufacturing firms in china. Journal of contemporary china, 16, 259-274.

[31]Sufian, F., and Habibullah, M., (2010). Developments in the efficiency of the Thailand banking sector: a DEA approach. Intelligent Journal of Development Issues, 9(3), 226-245.

[32]Brid, R., (2001). The prediction earnings movements using accounting data: an update and extension of OU and penman. Journal of Asset Management, 2, 196–199.

[33]Cummins, D., and Pnini, G., (2002). Optimal capital utilization by financial firm: evidence from the property liability insurance industry. Journal of Financial Services Research, 21, 10-21.

[34]Capobianco, H.M., and Fernandes, E.,(2004). Capital structure in the world airline industry. Transportation Research part A, 38, 421-434.

[35]Omero, M., Ambrosio, L., Pesenti, R., and Vialter, U., (2005). Multi attribute decision support system based on fuzzy logic for performance assessment. European Journal of Operational Research, 160(3), 710-72.

[36]Liang, G.S., Liu, C.F., Lin, W.C., and Yeh, C.H., (2006). A data development analysis of shipping industry band ratings. Tamkang Journal of science and engineering, 9(4),403-408.

[37]Duzakin, E., and Duzakin, H., (2007). Measuring the performance of manufacturing firms with super slack based model of date envelopment analysis, An Application of 500 major industrial enterprises in Turkish. European Journal of operational research, 182, 1412-1432.

[38]Margaritis, D., and psillaki, M., (2009). Capital structure equity ownership and firm performance. Journal of banking and financial, 30, 1-12.

[39]Siriopoulos C., Tziogkidis P., How do Greek banking institutions react after significant events? —A DEA approach. Omega 38 (2010) 294–308.

[40]Cheng, E., Chiang, Y.H., and Tang, B.S., (2007). Alternative approach to credit scoring by DEA Evaluating Browsers with respect PFI projects. Building and Environment, 42, 1752-1760.

[41]Cook W.D., Seiford L.M. (2009). Data Envelopment Analysis (DEA)—thirty years on. European Journal of Operational Research, 192, 1–17.

[42]Molhotra, D.K., and Molhotra, R., (2008). Analyzing financial statement using data envelopment analysis. Commercial Lending Review, 23, 25-31.