The Predictive Power of Credit Scores: Examining Default Probability in Taiwanese Credit Card Clients

Abstract

The concept of a scorecard originated from the need to establish a standardized and objective approach to evaluate credit applicants. Various techniques have been utilized to build scoring model. This research chooses Logistic regression to construct a scorecard using SPSS modeler. In this way, the study enhances the understanding of the relationship between credit scores and default behavior. By using a scorecard constructed through logistic regression, the study provides a comprehensive and interpretable model for evaluating creditworthiness. The study also employs linear probability models (LPM), logit, and probit models to assess the predictive power of credit scores on default probability. By utilizing these statistical techniques, the research presents robust empirical evidence on the significance of credit scores in predicting default behavior. Moreover, the research paper systematically analyzes prediction effects with and without control variables. By incorporating control variables such as demographic characteristics, the study adds depth to the understanding of scoring models. Overall, the findings provide valuable guidance for credit risk assessment practices and contribute to the ongoing development of effective credit evaluation frameworks.