A practical approach to credit scoring

Abstract

This paper proposes a DEA-based approach to credit scoring. Compared with conventional models such as multiple discriminant analysis, logistic regression analysis, and neural networks for business failure prediction, which require extra a priori information, this new approach solely requires ex-post information to calculate credit scores. For the empirical evidence, this methodology was applied to current financial data of external audited 1061 manufacturing firms comprising the credit portfolio of one of the largest credit guarantee organizations in Korea. Using financial ratios, the methodology could synthesize a firm's overall performance into a single financial credibility score. The empirical results were also validated by supporting analyses (regression analysis and discriminant analysis) and by testing the model' s discriminatory power using actual bankruptcy cases of 103 firms. In addition, we propose a practical credit rating method using the predicted DEA scores.

Chinese companies distress prediction: an application of data envelopment analysis

“…Both Emel et al (2003) and Min and Lee (2008) estimated a statistical model to predict DEA efficiency using the input and output financial ratios that could be used to classify out of sample cases.…”

“…Min and Lee (2008) estimated a CCR model (defined in the next section) with constant Returns to Scale and applied a cut-off to the efficiency score for each firm. The DEA score method performed less well than a linear discriminant function.…”

The Use of Genetic Algorithm, Clustering and Feature Selection Techniques in Construction of Decision Tree Models for Credit Scoring

“…Artificial neural networks (ANNs) [5], naive Bayes, logistic regression(LR), recursive partitioning, ANN and sequential minimal optimization (SMO) [6], neural networks (Multilayer feed-forward networks) [7], ANN with standard feed-forward network [8], credit scoring model based on data envelopment analysis (DEA) [9], back propagation ANN [10], link analysis ranking with support vector machine (SVM) [11], SVM [12], integrating non-linear graph-based dimensionality reduction schemes via SVMs [13], Predictive modelling through clustering launched classification and SVMs [14], optimization of k-nearest neighbor (KNN) by GA [15], Evolutionary-based feature selection approaches [16], comparisons between data mining techniques (KNN, LR, discriminant analysis, naive Bayes, ANN and decision trees) [17], SVM [18], intelligent-agent-based fuzzy group decision making model [19], SVMs with direct search for parameters selection [20], SVM [21], decision support system (DSS) using fuzzy TOPSIS [22], neighbourhood rough set and SVM based classifier [23], Bayesian latent variable model with classification regression tree [24], integrating SVM and sampling method in order to computational time reduction for credit scoring [25], use of preference theory functions in case based reasoning model for credit scoring [26], fuzzy probabilistic rough set model [27], using rough set and scatter search met heuristic in feature selection for credit scoring [28], neural networks for credit scoring models in microfinance industry [29].…”

Evaluating applicants’ credit capability for banking facilities by qualitative and operational indicators

“…They offered results on four credit datasets and compared their results with the performance of each individual classifier on predictive accuracy at various attribute noise levels. Min and Lee (2008) proposed a data envelopment analysis (DEA) for credit scoring and applied the method for current financial data of external audited 1061 manufacturing companies in Korea. Based on some financial ratios, the methodology could synthesize a company's overall performance into a single financial credibility score.…”