SAS Enterprise Miner

813 Words 4 Pages
Problem definition
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
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we can build a classification model to categorize bank loan applications as either safe or risky.
Firstly, the past data is used to estimate the classification rules. Then the classification rules can be applied to the new data. And finally, the results can help to support decision making( Gordon,2013 ).
In this case, the first thing is to built a classification model to identify those past default loan applications and classify the clients into four classes. After that, if there is a new clients, the bank need to put this client’s personal characteristics such as age, income or job in to the model and then arrange it into one of the classes. Finally, depending on the client’s class, they can decide to allow the application or reject it( Lin, Gan & Luo, 2008).

For example, there is a customer who is 50 years old. His monthly salary is 3000 RMB and doesn’t have any car or own house. And he also has a cancer and doesn 't have any wife and children. And if we analysis his information, through the classification rule, we can know that he would be put into the class 4 and the final result is high risk to default. His age is old and income is low. He doesn’t have any personal assets can be collateral but also have serious disease. These all factors makes his credit rating is low. Therefore, providing loan to him is quite risky, he has a high probability to break the contracts because he is unable to pay the

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