Abstract
Application of intelligent methods in banking becomes a challenging issue and acquiring special attention of banking supervisors and policy makers. Intelligent methods like rough set theory (RST), fuzzy set and genetic algorithm contribute significantly in multiple areas of banking and other important segments of financial sector. CAMEL is a useful tool to examine the safety and soundness of various banks and assist the banking regulators to ward off any potential risk which may lead to bank failure. RST approach may be applied for verifying authenticity and accuracy of CAMEL model and this chapter invites reader’s attention towards this relatively new and unique application of RST. The results of CAMEL model have been widely accepted by banking regulators for the purpose of assessing the financial health of banks. In this chapter we have considered ten largest public sector Indian banks on the basis of their deposit-base over a five-year period (2008–2009 to 2012–2013). The analysis of financial soundness of banks is structured under two parts. Part I is devoted to ranking of these banks on the basis of performance indices of their capital adequacy (C), asset quality (A), management efficiency (M), earnings (E) and liquidity (L). Performance analysis has been carried out in terms of two alternative approaches so as to bring implications with regard to their rank accuracy. We named these approaches as Unclassified Rank Assignment Approach and Classified Rank Assignment Approach. Part II presents analysis of accuracy of ranks obtained by CAMEL model for both the approaches that is for Unclassified Rank Assignment Approach and for Classified Rank Assignment in terms of application of Rough Set Theory (RST). The output of CAMEL model (ranking of banks for both approaches) is given as input to rough set for generating rules and for finding the reduct and core. The accuracy of the ranking generated by the CAMEL model is verified using lower and upper approximation. This chapter demonstrates the accuracy of Ranks generated by CAMEL model and decisions rules are generated by rough set method for the CAMEL model. Further, the most important attribute of CAMEL model is identified as risk-adjusted capital ratio, CRAR under capital adequacy attribute and results generated by rough set theory confirm the accuracy of the Ranks generated by CAMEL Model for various Indian public- sector banks.
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References
Ahn, B.S., Cho, S.S., Kim, C.Y.: The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Syst. Appl. 18(2), 65–74 (2000)
Arena, M.: Bank failures and bank fundamentals: A comparative analysis of Latin America and East Asia during the nineties using bank-level data. J. Bank. Finan. 32(2), 299–310 (2008)
Avkiran, N.K., Cai, L.C.: Predicting Bank Financial Distress Prior to Crises, Working Paper. The University of Queensland, Australia (2012)
Canbas, S., Cabuk, A., Kilic, S.B.: Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case. Eur. J. Oper. Res. 166(2), 528–546 (2005)
Das, S., Sy, A.N.R.: How Risky Are Banks’ Risk Weighted Assets? Evidence from the Financial Crisis, IMF Working Paper, 12/36 (2012)
Daubie, M., Leveck, P., Meskens, N.: A comparison of the rough sets and recursive partitioning induction approaches: An application to commercial loans. Int. Trans. Oper. Res. 9, 681–694 (2002)
Demirguc-Kunt, A., Detragiache, E., Gupta, P.: Inside the crisis: An empirical analysis of banking systems in distress. J. Int. Money Finan. 25(5), 702–718 (2006)
Demyanyk, Y., Hasan, I.: Financial crises and bank failures: A review of prediction method. OMEGA 38(5), 315–324 (2010)
Estrella, A., Park, S.: Capital ratios as predictors of bank failure. Econ. Policy Rev. 6(2), 33–52 (2000)
Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129, 1–47 (2001)
Hassanien, A.Q., Zamoon, S., Hassanien, A.E., Abrahm, A.: Rough set generating prediction rules for stock price movement. In: Computer Modeling and Simulation, EMS ′08. Second UKSIM European Symposium, pp. 111–116 (2008)
Khoza, M., Marwala, T.: A rough set theory based predictive model for stock prices. In: Proceeding of IEEE 12th International Symposium on Computational Intelligence and Informatics, pp. 57–62. Budapest (2011)
Kolari, J., Glennon, D., Shin, H., Caputo, M.: Predicting large US commercial bank failures. J. Econ. Bus. 54(4), 361–387 (2002)
Lanine, G., Rudi, V.V.: Failure predictions in the Russian bank sector with logit and trait recognition models. Expert Syst. Appl. 30(3), 463–478 (2006)
Le Lesle, V., Avramova, S.: Revisiting Risk-Weighted Assets, IMF Working Paper, 12/90 (2012)
Mannasoo, K., Mayes, D.G.: Investigating the Early Signals of Banking Sector Vulnerabilities in Central and East European Emerging Markets, Working Paper of Eesti Pank, p. 8 (2005)
Mariathasan, M., Merrouche, O.: The Manipulation of Basel Risk-Weights. Evidence from 2007–2010. University of Oxford, Department of Economics, Discussion Paper, p. 621 (2012)
Nursel, S.R., Fahri, U., Bahadtin, R.: Predicting bankruptcies using rough set approach: The case of Turkish bank. In: Proceeding of American Conference on Applied Mathematics (Math ′08), Harvard, Massachusetts, USA, 24–26 Mar 2008
Ooghe, H., Prijcker S.D.: Failure Processes and Causes of Company Bankruptcy: A Typology, Working Paper, Steunpunt OOI (2006)
Pawlak, Z.: Rough set approach to knowledge-based decision support. Eur. J. Oper. Res. 99, 48–57 (1997)
Pawlak, Z.: Rough sets. Int. J. Comput. Int. Sci. 11(3), 341–356 (1982)
Poghosyan, T., Cihák, M.: Distress in European Banks: An Analysis Based on a New Dataset. IMF Working Paper, 09/9 (2009)
Prasad, K.V.N., Ravinder, G.: A camel model analysis of nationalized banks in India. Int. J. Trade Commer. 1(1), 23–33 (2012)
Reyes, S.M., Maria, J.V.: Modeling credit risk: An application of the rough set methodology. Int. J. Bank. Finan. 10(1), 34–56 (2013)
Rodriguez, M., Díaz, F.: La teoría de los rough sets y la predicción del fracaso empresarial. Diseño de un modelo para pymes, Revista de la Asociación Española de Contabilidad y Administración de Empresas 74, 36–39 (2005)
Segovia, M.J., Gil, J.A., Vilar, L., Heras, A.J.: La metodología rough set frente al análisis discriminante en la predicción de insolvencia en empresas aseguradoras. Anales del Instituto de Actuarios Españoles 9 (2003)
Tatom, J., Houston, R.: Predicting Failure in the Commercial Banking Industry. Networks Financial Institute at Indiana State University. Working Paper, p. 27 (2011)
Tung, W.L., Quek, C., Cheng, P.: Genso-Ews: A novel neural-fuzzy based early warning system for predicting bank failures. Neural Netw. 17(4), 567–587 (2004)
Wheelock, D.C., Wilson, P.W.: Why do banks disappear? The determinants of U.S. bank failures and acquisitions. Rev. Econ. Stat. 82(1), 127–138 (2000)
Xu, J.N., Xi, B.: AHP-ANN based credit risk assessment for commercial banks. J. Harbin Univ. Sci. Technol. 6, 94–98 (2002)
Yu, G.A., Xu, H.B.: Design and implementation of an expert system of loan risk evaluation. Comput. Eng. Sci. 10, 104–106 (2004)
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Vashist, R., Vashishtha, A. (2015). An Investigation into Accuracy of CAMEL Model of Banking Supervision Using Rough Sets. In: Azar, A., Vaidyanathan, S. (eds) Computational Intelligence Applications in Modeling and Control. Studies in Computational Intelligence, vol 575. Springer, Cham. https://doi.org/10.1007/978-3-319-11017-2_1
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