SELECTION BIAS REDUCTION IN CREDIT SCORING MODELS

Credit risk refers to the potential of the borrower to not be able to pay back to investors the amount of money that was loaned. For loans to individuals or small businesses, credit risk is typically assessed through a process of credit scoring. For these purposes, credit scoring models are built. It involves using different statistical techniques and historical data from the accepted applicants. However, the scorecard is designed to be used on all applicants and therefore parameter estimates of credit risk models may be biased due to the selection bias. Reject inference is a technique that tries to mitigate the consequences of this phenomenon. One of the possibilities how selection bias can be reduced is to grant loans to a part of rejected applicants and analyse their behaviour (enlargement method). This approach is time-consuming and costly especially. We introduced a modification of the method with the costs optimization. Our results show that involving rejected cases positively affects forecast accuracy of credit score as well as the discriminative power of models. Finally, we discuss the expected costs and benefits of the modified approach.