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In this study, the author classifies Credit Card Default Model into two models, Applicant Model and Behavior Model, according to the time in which the model is implemented. Applicant Model, is adopted when customers apply for credit cards, while Behavior Model is utilized when credit of customers is reviewed, credit lines are adjusted, or the duration of the credit is extended. The process in which Credit Card Default Model is built comprise several steps, such as data collection, data cleaning, factor analysis, and adjustments to the model, etc. This study also probes into the relationship between various factors in credit ranking and default probability, and thus derives model calibration function and rating grade.
This study discovered factors in Applicant Model that can differentiate customers who tend to repay from those whose credits would turn delinquent. Among these factors are customers’ basic profiles, records from Joint Credit Information Center and transaction behavior. This study also discovered factors in Behavior Model that can distinguish between those who tend to pay off and those who would fail to follow through their obligations. Among those factors are customers’ transaction behavior, payment records, use of credit lines, balance of revolving credits, and classification of consumption behavior, etc.
This study measures customer by 26 grades. The higher the grade, the greater the likelihood of default in the coming one year. Future studies may further develop Loss Given Default Model and Exposure at Default Model. Based on expected loss and minimum capital requirement, loans can be priced more accurately. Various marketing strategies can be targeted at different groups of customers, thus fomenting loyalty among customers. Through the understanding of the relationship between factors and risk in credit cards, banks can identify less risky customer groups, therefore making more profits.
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