| Summary: | 碩士 === 國立成功大學 === 統計學系碩博士班 === 93 === With New Basel II Accord going to take effect at the end of 2006, many efforts have been made in Taiwan both at the public and private sectors in order to be confronted with the foreseeable challenges. To this end governmental ministry officers, along with the banking industry, have been drafting various strategic plans and proposals to hopefully cope with the revolutionary change. Therefore, the banking industry has become more and more conscious of risk management issues.
Parallel to this change is the rapid growth of personal consumption credits in recent years, which could cause national credit crisis. According to the statistics published by the Financial Monitoring Committee of the Executive Yuan, the number of credit cards in use had reached 441,820 (in thousands) in December 2004. If this is calculated against the population of people over twenty years of age, the number is 16,709 (in thousands); that is, each adult has approximately an average of 2.64 credit cards. By January 2005, the number of credit cards in use had grown to 445,110 (in thousands) with a circulating credit of 463,600 million dollars, a total signed payment of 125,000 million dollars, and a cash loan of 19,600 million dollars, all with incredible growth rate. To deal with the problem of credit risk, this study tries first to find factors affecting the quality of loan, and then build up criteria for customer credit assessment. Taking car loan as an example, the author explores the nature of risk in personal consumption credits using statistical methods such as chi-square test for independence, multinomial logistic regression for credit prediction, survival data analysis, and decision tree.
The research result shows that there are six important factors involved in a loan risk. They are: occupation, sex, credit amount, credit ratio, customer age, and car price. With these six factors extracted, the author uses odds ratio to build up the assessment criteria, and then applies quartile deviation to divide the matrix into three equal parts, namely, A, B, and C with A being the most credit customers and C the worst credit customers. In the final stage of the research, the C class customers are analyzed by the regression tree. It is found that “age” is a reliable variable to ensure an acceptable credit quality for crediting.
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