A multicriteria decision support system for bank rating

a r t i c l e i n f o Bank rating refers to the analysis of a bank's overall viability, performance and risk exposure. Within the recent financial turmoil, bank rating has become extremely important. Typically, bank rating is performed through empirical procedures that combine financial and qualitative data into an overall performance index. This paper presents a case study on the implementation of a multicriteria approach to bank rating. The proposed methodology is based on the PROMETHEE II method implemented in an integrated decision support system. Special emphasis is put on the sensitivity of the results with regard to the relative importance of the evaluation criteria and the parameters of the evaluation process. Banks have a prominent role in the financial and business environment. The increasing risks that banks face, have led to the introduction of the new regulatory framework of Basel II, which defines the core principles for financial risk management in banking institutions. One of the pillars of this framework involves the banking supervision process. The central banks that are responsible for supervising the banks in each country use rating systems to assess the soundness of the banks. According to Sahajwala and Van den Bergh [29], the emphasis is put on the development of formal, structured and quantified assessments taking into account the financial performance of banks as well as their underlying risk profile and risk management capabilities. Such assessments support the supervisors and examiners in identifying changes in banks' condition as early as possible. Due to lack of sufficient historical data about bank defaults, bank rating systems are usually based on empirical assessment techniques. Sahajwala and Van den Bergh [29] provide an extensive overview of several systems, which are currently used in practice. The most popular approach is based on the CAMELS framework, which involves the consideration of six major factors: Capital, Assets, Management, Earnings, Liquidity, and Sensitivity to market risk. Specific criteria within these categories are usually aggregated in a simple weighted average model. Several multicriteria techniques have also been used for the evaluation of bank performance. Kosmidou and Zopounidis [18], Mareschal and Brans [21], as well as Mareschal and Mertens [22] used the PROMETHEE method, Spathis et al. [30] and Zopounidis et al. [33] used disaggregation techniques, Raveh [26] used the Co-plot method, whereas Ho [13] applied grey relational analysis, and Garcia et al. [9] used goal programming. Several data envelopment analysis …