Using Semi-Supervised Classifiers for Credit Scoring
Document Type Article
Accepted (Feb 2011) for publication in the Journal of the Operational Research Society.
Abstract
In credit scoring, low-default portfolios 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 algorithms as a solution to the low-default portfolio problem are evaluated.
The performance of one-class classification algorithms is compared with the performance of supervised two-class classification algorithms. This study also investigates the suitability of oversampling, which is a common approach to dealing with low-default portfolios. Assessment of the performance of one-and-two-class classification algorithms using nine real-world banking data sets, which have been modified to replicate low-default portfolios, is provided. Our results demonstrate that only in the near or complete absence of defaulters should semi-supervised one-class classification 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.