Assessing Bank Performance with Operational Research and Artificial Intelligence Techniques: A Survey Meryem Duygun-Fethi (School of Management, University of Leicester) Fotios Pasiouras (School of Management, University of Bath)
University of Bath School of Management, Working Paper Series 2009.02
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Assessing Bank Performance with Operational Research and Artificial Intelligence Techniques: A Survey Meryem Duygun Fethi1*, Fotios Pasiouras2
School of Management, University of Leicester, UK 2 School of Management, University of Bath, UK
Abstract This paper presents a comprehensive review of 179 studies which employ operational research (O.R.) and Artificial Intelligence (A.I.) techniques in the assessment of bank performance. We first discuss numerous applications of data envelopment analysis which is the most widely applied O.R. technique in the field. Then we discuss applications of other techniques such as neural networks, support vector machines, and multicriteria decision aid that have also been used in recent years, in bank failure prediction studies and the assessment of bank creditworthiness and underperformance.
Keywords: Artificial Intelligence, Banks, Data Envelopment Analysis, Operational Research, Literature review
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1. Introduction Banks play a central role in the economy. They keep the savings of the public and finance the development of business and trade. Furthermore, numerous studies argue that the efficiency of financial intermediation affects economic growth while others indicate that bank insolvencies can result in systemic crises which have adverse consequences for the economy as a whole.1 Thus, the performance of banks has been an issue of major interest for various stakeholders such as depositors, regulators, customers, and investors. While bank performance has been traditionally evaluated on the basis of financial ratios, advances in operational research (O.R.) and artificial intelligence (A.I.) have resulted in a shift towards the use of such state-of-the-art techniques. Of course, this is not surprising, since O.R. has been extensively applied to finance during the last half century (Board et al., 2003). This paper presents a comprehensive review of the use of OR and AI techniques in the assessment of bank performance. The rest of the paper is structured as follows. Section 2 positions the survey within the existing literature and discusses our framework. Section 3 discusses applications of data envelopment analysis (DEA) in the estimation of bank efficiency and productivity growth. Section 4 presents applications of other O.R. and A.I. techniques in the prediction of bank failure and the assessment of bank creditworthiness and underperformance. Section 5 summarizes our conclusions.
2. Scopus and framework There are several interesting reviews that are related to our survey. For example, Cook and Seiford (2009) review the methodological developments of DEA over the...
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