A Classification Model That Can Help Provide The Loan Or Not, Who And How Much Money They Can
There is a local Chinese commercial bank which named Bank of Chengdu provide loans to public. This bank has already provided thousands of loan services in the past several years.
However, in the tax relief reasons, the bank has responded positively to the call of the province government, providing loans with lower interest to the low-income people. This led to a high default rate. About 31% of those people who apply for loans are in arrears. Therefore, there are some problems that how does the bank choose to provide the loan or not, who and how much money they can provide. The objective in this project is to use the SAS Enterprise Miner to develop a classification model that can help to support the decision-making for the bank to analysis credit risk about customers. In this process, it would use lots of data to select some variables to help establish the model and determine which factors influence the loan applicant in arrears.
The data we got only from one city, but cities and cities are different. Some are rich and developed, and some are not. This data from the city may cannot to represent the whole province. The composition of the loan applicants is significantly featured by the economic development status. Because of the policy requirement and tax incentives, some loan applicants didn’t meet the loan requirements, they still received the money. Therefore there is no indicator in the dataset to show which applicants were benefit from the…