Adaptive consumer credit classification

Abstract

Credit scoring methods for predicting creditworthiness have proven very effective in consumer finance. In light of the present financial crisis, such methods will become even more important. One of the outstanding issues in credit risk classification is population drift. This term refers to changes occurring in the population due to unexpected changes in economic conditions and other factors. In this paper, we propose a novel methodology for the classification of credit applications that has the potential to adapt to population drift as it occurs. This provides the opportunity to update the credit risk classifier as new labelled data arrives. Assorted experimental results suggest that the proposed method has the potential to yield significant performance improvement over standard approaches, without sacrificing the classifier's descriptive capabilities.

Keywords:

• credit scoring
• logistic regression
• population drift
• online learning
• H-measure

Acknowledgements

We wish to thank the editor and two anonymous referees for their valuable comments and suggestions which greatly improved this paper. This research was undertaken as part of the ALADDIN (Autonomous Learning Agents for Decentralised Data and Information Networks) project and is jointly funded by a BAE Systems and EPSRC (Engineering and Physical Research Council) strategic partnership, under EPSRC grant EP/C548051/1. David J. Hand was partially supported by a Royal Society Wolfson Research Merit Award. We are grateful to the UK bank that provided the data.