Integrating External Credit Scores in Internal Credit Risk Models

Sammanfattning

From both a legal and business perspective, it is important for credit institutes to accurately assess the risk of default for potential borrowers. This is traditionally done using statistical methods, such as logistic regression or decision trees, utilising sociodemographic and credit history information.This thesis investigates whether a bureau score, provided by a credit bureau, should be incorporated as an internal variable within the credit risk model, referred to as a model-in-model approach, or used as a standalone model and combined with an internal model through ensemble techniques. The two approaches are evaluated using logistic regression and XGBoost on a dataset of real loan applications from a specific market. The logistic regression models utilise Weight of Evidence transformed variables, and the XGBoost models use non-transformed features. Model performance is evaluated using the discriminatory power measures Gini and KS on 100 bootstrap samples. Both Gini and KS are consistently higher for the model-in-model approach across the bootstrap samples. However, the evidence is less conclusive as to whether this result generalises to future datasets.