Articlegreen2011

CREDIT SCORING, STATISTICAL TECHNIQUES AND EVALUATION CRITERIA: A REVIEW OF THE LITERATURE

409 citations0/0 files storedIntelligent systems in accounting, finance and management/Intelligent systems in accounting, finance & management

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Financial Distress and Bankruptcy Prediction Credit Risk and Financial Regulations Imbalanced Data Classification Techniques

Abstract

SUMMARY Credit scoring has been regarded as a core appraisal tool of different institutions during the last few decades and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria, are investigated in the literature. The principal aim of this paper, in general, is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas but in particular primarily in finance and banking. This paper also aims to investigate how credit scoring has developed in importance and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients' behaviour. Finally, this paper also suggests a number of directions for future research. Copyright © 2011 John Wiley & Sons, Ltd.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1990Econometric Models and Economic Forecasts 1962Applied Statistical Decision Theory 1992Managerial Applications of Neural Networks: The Case of Bank Failure Predictions 1994Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2000Neural network credit scoring models 2003Benchmarking state-of-the-art classification algorithms for credit scoring

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