Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Abstract

SummaryCredit 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 is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas, in general, but primarily in finance and banking, in particular. 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.

Strategic Decision Making Of Micro-Lenders: An Innovative Business Model For Risk Management

“…Credit scoring is defined as the process of a model that evaluates if someone is creditworthy or not (Hand & Jacka (1998) as cited in Abdou & Pointon, 2011). Credit scoring is a discipline which was developed and implemented since the beginning of 1960s.…”

On the suitability of resampling techniques for the class imbalance problem in credit scoring

“…Standard performance evaluation criteria in the fields of credit soring include accuracy, error rate, Gini coefficient, Kolmogorov-Smirnov statistic, mean squared error, area under the ROC curve, type-I error and type-II error (Thomas et al 2002, Yang et al 2004, Hand 2005, Abdou and Pointon 2011. For a two-class problem, most of these metrics can be easily derived from a 2 × 2 confusion matrix as that given in Table 2, where each entry (i, j) contains the number of correct/incorrect predictions.…”

The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast

“…), credit scoring models have become a standard technique for credit risk evaluation and estimation of the probability of default, and according to Bailey (2004) are now one of the most popular models used in finance in general. For further discussion on the history of credit scoring and associated issues see Chandler, Coffman (1979), Crook (1996), Thomas (2000), or Abdou, Pointon (2011).…”

“…Due to its simple and intuitive character, as well as the relatively good results it provides, logistic regression has maintained its position as a standard tool even after more sophisticated models were developed, such as neural networks, support vector machines, genetic algorithms and various hybrid and ensemble models. For an overview see Abdou, Pointon (2011), Li, Zhong (2012), or Lessmann et al (2015).…”