As the year end or the annual tax season approaches, many banks increase advertising and promotion in personal loans for those in need of small loans. The revenue derived from these loans also became a main source of profit for these banks. This research exams the approved borrowers’ credit status from a sample bank. By using SPSS Clementine 12.0 data mining software to create sample models based on overall correct rate and overdue predictive rate, the best model will be determined based on classification model evaluation. The final chosen model will expedite lenders’ classification of small loan borrower and assist the underwriter in reduce lenders’credit risks.
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