Identifying Potential Default Loan Applicants - A Case Study of Consumer Credit Decision for Chinese Commercial Bank 1

Consumer credit is a lucrative but risky business. In order to control risk and maximize profits, commercial banks around the world have made great efforts to develop various analytic models to identify potential default loan applicants. This is also critically important to China, as the non-performing individual loans of Chinese commercial banks have been fast growing because of the myopic business attitude and the overheated economic growth. To prevent the US sub-prime kind of crisis in China, Chinese commercial banks are adopting advanced analytic means to help make loan decisions. This paper reports a data mining application in the analysis of default loan applicants using a real dataset consisting of 641,988 observations obtained from a Chinese commercial bank, located in the southwest of China. An exploratory study of the dataset led to a number of interesting statistic figures that may characterize the applicants in the western region of China. In the analytic study, we constructed two types of models, unweighted and weighted, with SAS® Enterprise Miner. The models have revealed a number of useful findings that meet our expectation. They demonstrated good predictive power for loan decision making. The success of the study has consoled our concern in the data quality that is relevant to the efficiency of the data collection system rooted in the existing Chinese business system.