Rule Based Predictions for Loan Defaults of Used Cars Based on DRSA and FCA

Abstract

Numerous algorithms and frameworks have been proposed by scholars to solve credit scoring problems in the past. Only a few studies have examined the factors affecting second car loan default. However, this issue is of great importance to the auto loan industry. Therefore, this study intends to define a hybrid multi-criteria decision making (MCDM) model to mine the database of defaulting customers of loans of second hand cars. First, this study introduces the Dominance Based Rough Set Approach (DRSA) to analyze the characteristics of the defaulting clients, derive the core attributes as well as the decision rules. Then, the Formal Concept Analysis (FCA) is adopted to derive the main concepts affecting the default of auto loans. The empirical results can be used as a reference for auto loan companies. Based on the database of one of major financial institutions in Taiwan, the feasibility of the analytic framework was verified. According to the mining results of the customer database, age, gender, marital status, education, income and loan amount are the core attributes, and 15 decision rules are derived. The results of this study can be used as a basis for future loan verification by financial institutions, as well as for the introduction of intelligent automatic loan verification mechanism and the development of intelligent vehicle loan platform.