| Summary: | 碩士 === 國立交通大學 === 工業工程與管理系所 === 97 === In recent years, subprime mortgage crisis in U.S.A. has resulted in global economical recession. Due to the huge debts, thousands of enterprises went bankrupt and financial institutions suffered serious loan risk. In order to enhance the reactive capability of financial institutions when facing credit risk, the New Basel Capital Accord (Basel II) issued by the Bank for International Settlements allows financial institutions to construct their own internal measures for credit risk. Therefore, financial institutions can adopt some emergency measures and strategies to deal with loan risk. Many risk assessment models have been developed in many studies. However, It is usually too complicated and time-consuming to employ the developed risk assessment models for banks or financial institutions. The Group Method of Data Handling (GMDH) method can be easily applied in practice since it can develop an effective discriminant function. Therefore, this paper proposes a multiple-stage risk assessment model using GMDH method to evaluate the credit rank and loan strategies based on the outcomes from each stage of the proposed model. Finally, real cases from Taiwanese small-and-median sized enterprises and UCI Repository of Machine Learning database are utilized to demonstrate the effectiveness of the proposed multiple-stage risk assessment model.
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