A decision rule-based soft computing model for supporting financial performance-improvement of the banking industry Kao-Yi Shen1 and Gwo-Hshiung Tzeng2* 1
Department of Banking and Finance, Chinese Culture University (SCE), Taipei, Taiwan Email:
[email protected];
[email protected] 2 Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, 151, University Rd., San Shia District, New Taipei City, 23741, Taiwan E-Mail:
[email protected]
Abstract. This study attempts to diagnose the financial performance-improvement of commercial banks by integrating suitable soft computing methods. The
diagnosis
of
financial
performance-improvement comprises of three parts: prediction, selection and improvement. The performance prediction problem involves many criteria, and the complexity among the interrelated variables impedes researchers to discover patterns by conventional statistical methods. Therefore, this study adopts a dominance-based rough set approach (DRSA) to solve the prediction problem, and the core attributes in the obtained decision rules are further processed by an integrated multiple criteria decision-making (MCDM) method to make selection and to devise improvement plans by using VIKOR method combining influential weights of DANP to reduce gaps of each criterion for achieving aspiration level. The retrieved attributes (i.e., criteria) are used to collect the knowledge of domain experts for selection and improvement. This study uses the data (from 2008 to 2011) from the central bank of Taiwan for obtaining decision rules and forming an evaluation model; furthermore, the data of five commercial banks in 2011 and 2012 are chosen to evaluate and improve the real cases. In the result, we found the top ranking bank outperformed the other four banks, and its performance gaps for improvements were also identified, which indicates the effectiveness of the proposed model.
Keywords: Rough set approach (RSA), Dominance-based rough set approach (DRSA), DEMATEL-based ANP (DANP), VIKOR, Multiple criteria decision making (MCDM).
*
Corresponding author: G.-H. Tzeng (Distinguished Chair Professor); Email:
[email protected]
