Credit Risk Scoring Model for Consumer Financing: Logistic Regression Method

Abstract

This chapter shows how to identify the characteristics of borrowers that are part of a credit scoring model. The credit risk scoring model is an important tool for evaluating credit risk associated with customer characteristics that affect defaults. This research was conducted at a financial institution, a subsidiary of a commercial bank in Indonesia, to answer the challenge of determining the feasibility of providing financing quickly and accurately. This model uses a logistic regression method based on customer data with indicators of demographic characteristics, assets, occupations, and financing payments. This study identifies nine variables that meet the goodness of fit criteria, which consist of WOE, IV, and p-value. The nine variables can be used as predictors of default probability: type of work, work experience, net finance value, tenor, car brand, asset price, percentage of down payment (DP), interest, and income. The results of the study form a risk assessment model to identify variables that have a significant effect on the probability of default.

Citation

Ismawati, I.Y. and Faturohman, T. (2023), "Credit Risk Scoring Model for Consumer Financing: Logistic Regression Method", Barnett, W.A. and Sergi, B.S. (Ed.) Comparative Analysis of Trade and Finance in Emerging Economies (International Symposia in Economic Theory and Econometrics, Vol. 31), Emerald Publishing Limited, Leeds, pp. 167-189. https://doi.org/10.1108/S1571-038620230000031023