Bankruptcy prediction in banks and firms via statistical and intelligent techniques – A review

• Published in: European journal of operational research Vol. 180; no. 1; pp. 1 - 28
• Main Authors: Ravi Kumar, P., Ravi, V.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2007
• Subjects: Bankruptcy prediction; Banks; Case-based reasoning; Decision trees; Evolutionary approaches; Firms; Fuzzy logic; Intelligent techniques; Neural networks; Operations research; Rough sets; Statistics; Support vector machine and soft computing; Operations research; Case-based reasoning; Support vector machine and soft computing; Statistics; Fuzzy logic; Neural networks; Rough sets; Bankruptcy prediction; Banks; Decision trees; Evolutionary approaches; Firms; Intelligent techniques
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2006.08.043

This paper presents a comprehensive review of the work done, during the 1968–2005, in the application of statistical and intelligent techniques to solve the bankruptcy prediction problem faced by banks and firms. The review is categorized by taking the type of technique applied to solve this problem as an important dimension. Accordingly, the papers are grouped in the following families of techniques: (i) statistical techniques, (ii) neural networks, (iii) case-based reasoning, (iv) decision trees, (iv) operational research, (v) evolutionary approaches, (vi) rough set based techniques, (vii) other techniques subsuming fuzzy logic, support vector machine and isotonic separation and (viii) soft computing subsuming seamless hybridization of all the above-mentioned techniques. Of particular significance is that in each paper, the review highlights the source of data sets, financial ratios used, country of origin, time line of study and the comparative performance of techniques in terms of prediction accuracy wherever available. The review also lists some important directions for future research.