Data mining applied to SME Credit Guarantee Credit Risk Prediction~ A Case Study of T Bank

• Main Authors: Jen-chung, Pan, 潘仁忠
• Other Authors: Ben-Chang, Shia
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/zw8j2w

碩士 === 輔仁大學 === 統計資訊學系應用統計碩士在職專班 === 103 === The Asian Development Bank estimates that Asia will increasingly become the focus of global economic development, and therefore Financial Supervisory Commission R.O.C （FSC）expect Domestic banks to continue improving their asset quality in order to grasp the business opportunities in fast-growing economies of Asia.Although T Bank has good credit quality assets, in order to achieve the expectations of the FSC, screening out the small business credit guarantee（SMEG）loans as the object to be improved, because of its overdue loan ratio remains high.By collecting SMEG loans 2,474 cases of T Bank from 2011 to 2013, including 80 overdue cases within a year after receiving loans, as well as to pay interest repayment of principal of the cases were normal 2,394 cases.With the above cases in the financial information and non-financial information before grant loans one year, selected key variables affect the SMEG loans overdue, and use Data Mining methods to establish an appropriate forecasting model of overdue.The test results, "the overdue case" and "normal case" of 1: 2 ratio of oversampling constructed with Logistic Regression model, both forecast accuracy and stability, assessed as the best model of this study, and this model can be increased 42.56% probability screened overdue short-term case than not compared to any model prediction.