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Adaptive credit scoring using local classification methods

Nikolaidis Dimitrios

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Summary

Despite the advances in machine learning methods which are also applied in credit scoring with overall positive results, there are still very important unresolved issues, pertaining not only to academia but to practitioners and the industry as well, such as model drift as an inevitable consequence of population drift and the strict regulatory obligations for transparency and interpretability of the automated profiling methods. We present a novel adaptive behavioral credit scoring scheme which uses online training for each incoming inquiry (a borrower) by identifying a specific region of competence to train a local model. We compare different classification algorithms i.e. logistic regression with state of the art machine learning methods (random forests and gradient boosting trees) that have shown promising results in the literature machine learning). Our data sample has been derived from a proprietary credit bureau database and spans a period of 11 consequent years with quarterly sampling frequency consisting of more than 3,520,000 record-month observations. Rigorous performance measures used in credit scoring literature and practice (such as AUROC and H-Measure) indicate that our approach deals effectively with population drift and that local models outperform their corresponding global ones in all cases. Furthermore, when using simple local classifiers such as logistic regression we can achieve comparable results with the global machine learning ones which are considered “black box” methods

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