Construction of Credit Risk Assessment Model for Car Leasing by SMOTE and Two-Stage Logistic Regression Techniques
|關鍵字:||信用風險評估模型;邏輯斯迴歸;增生少數合成技術;類別不對稱;credit risk assessment model;logistic regression;synthetic minority over-sampling technique;category asymmetry|
In traditional car leasing industry, most of the case approvals are conducted by car-leasing appraisers based on the information provided by the applicants. This process is not only time-consuming but also subjective to appraisers’ personal and professional experiences. As a result, it may lead to wrongfully approving a case which turned into default or rejecting a good-quality applicant. Objectively using quantitative methods to measure credit risks of loan applicants by financial institutes has been widely applied and accepted. However, literatures regarding using such methods in making car-leasing decisions are rare which may due to the fact that that real data is not easy to collect and also lack of study in discussing the variables to effectively evaluating the credit risk of car-leasing applicants. As a result, this research aims at assessing the credit risk of car-leasing industry by using quantitative model. This study first applied the synthetic minority over-sampling technique (SMOTE) to resolve the class-imbalance problem found in the data since it was found that the number of good-credit applicants are a lot more than the bad-credit ones. Furthermore, this study designed a two-stage method applied Logistic regression in each stage to enhance the effect of the risk assessment model. A set of real data of car-leasing applications provided by a financial organization in Taiwan is used to demonstrate the effectiveness of efficiency of the propose model. The results shown that SMOTE was more effective than over- or under sampling methods in terms of resolving the class imbalance problem found in the data. Moreover, the variables chosen in this study for model building along with the proposed approach were be able to objectively assess the credit risk of the car-leasing applicants.