| Summary: | 碩士 === 中原大學 === 企業管理研究所 === 98 === The customer business loans relative to the enterprise loans and secured loans, its number is very huge, but each amount is small, if has not observed its borrower violation situation independently, possible only pay attention to sum of the enterprise loans and secured loans，dilutes the DPD ratio of the personal unsecured, thus neglects the policy of personal unsecured loan .
Especially,since the card debt storm impact the banks, the total loan balance of credit card, cash card and personal unsecured loans has gone down , no significant performance. Facing the personal unsecured which day by day worsens loans, if has not control the risk , in the future the bank must add raises the allowance for uncollectible account, affects the bank the income.
Specifically speaking "the negotiation" the original intention has frontage to be positive, when the customer credit has the danger signal, the bank must confirm and investigate the risk source ,maybe is the customer credit expands improperly or income structural change, also perhaps has the moral hazard.
Actually for the banks, the negotiation program is spends the cost and the time .According to the former real results, the number of continued normal payment very low percentage of total that the customers apply to negotiation, this research wants to discuss; 1. The application that trouble debt customers may know accurately which one is the true ability and the sincerity, 2. The application that trouble debt customers may know accurately which one is not the true ability and the sincerity in advance.Bank will not give accepts, and restricted cost and manpower utilization to correct negotiation customer.
This research by the Data Mining method of “Decision Tree” &“ Artificial Neural Network”two kind of models, Traditional Statistics method of “Discriminated analysis” ，comparison of test results and analyze the reasons . Building forecast of model may recognize has the latent success of negotiation customer .
The findings showed that in 27 research variables, any two kind of models altogether has the variables total of 7 , respectively was "the Principal balance /amount", "predetermined the payment date", "the interest rate", " Installments amount ", "the unemployment rate (%)", "to pay for above normally 6 months", "the loan type", The model has separate variables to have 5 items "the date of birth", "the restructuring remark", "the occupation", "the school record", "the mailing address", bvarious Rate of accuracy (%) compared on, in training value by “ Artificial Neural Network” 96.07% most superior, next is "Decision Tree" 92.94%, in test value by “Decision Tree” most superior 92.53%, next is “ Artificial Neural Network”92.47%, the rate of accuracy reaches above 90%, but “Discriminated analysis” in primitive and the overlapping confirmation's accuracy respectively is 75.15%, 75.14%.The rate of accuracy has a big difference of both technology methods .
Knew by the findings, this research forecast to default risk of negotiation on trouble debt restructuring of customer bussiness loans, the Data Mining method is suited.
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