Articlegreen2012

Using semi-supervised classifiers for credit scoring

Kenneth Kennedy · Brian Mac Namee · Sarah Jane Delany

38 citations0/0 files storedJournal of the Operational Research Society

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Financial Distress and Bankruptcy Prediction Anomaly Detection Techniques and Applications Imbalanced Data Classification Techniques

Abstract

In credit scoring, low-default portfolios (LDPs) are those for which very little default history exists. This makes it problematic for financial institutions to estimate a reliable probability of a customer defaulting on a loan. Banking regulation (Basel II Capital Accord), and best practice, however, necessitate an accurate and valid estimate of the probability of default. In this article the suitability of semi-supervised one-class classification (OCC) algorithms as a solution to the LDP problem is evaluated. The performance of OCC algorithms is compared with the performance of supervised two-class classification algorithms. This study also investigates the suitability of over sampling, which is a common approach to dealing with LDPs. Assessment of the performance of one- and two-class classification algorithms using nine real-world banking data sets, which have been modified to replicate LDPs, is provided. Our results demonstrate that only in the near or complete absence of defaulters should semi-supervised OCC algorithms be used instead of supervised two-class classification algorithms. Furthermore, we demonstrate for data sets whose class labels are unevenly distributed that optimising the threshold value on classifier output yields, in many cases, an improvement in classification performance. Finally, our results suggest that oversampling produces no overall improvement to the best performing two-class classification algorithms.

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References

2000Neural network credit scoring models 2003Benchmarking state-of-the-art classification algorithms for credit scoring 2002Credit Scoring and Its Applications 2007Using neural network ensembles for bankruptcy prediction and credit scoring 2017Credit Scoring and Its Applications 2008An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring 1996A k-Nearest-Neighbour Classifier for Assessing Consumer Credit Risk 1999Financial credit-risk evaluation with neural and neurofuzzy systems

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