Constructing an Integrated Credit Risk Assessment Model

• Main Authors: Cheng, Wei-Hao, 程偉皓
• Other Authors: Chang, Yung-Chia
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/03898996914582122640

碩士 === 國立交通大學 === 工業工程與管理系所 === 101 === The importance of investigating the credit of borrowers is highly considered among current financial organizations. Most of the financial risks data in present distinguished into 2 different cases, which are: default and non-default. The credit risk assessment model is widely accepted in most of financial industries to evaluate the degree of risk impact of borrowers. There are a number of credit risk assessment models for any specific applied areas, most financial organizations construct numerous assessment models by history data. The effects of risk with different data character do not have the same behavior in the same model. Since the lack of analysis in the relations between data characters and model mythologies, most organizations use try-error method to classify the most accurate model as adapted model. While the accuracy in models do not have significant difference, the suggested decisions may different. This paper provides an integrated credit risk assessment model when the differences of multiple models are trivial. Based on the consequences of various models, formulating fuzzy membership function generates new decision result. An anonymous financial organization is as example in this research. Applied Group Method of Data Handing (GMDH), k-Nearest Neighbor algorithm (KNN) and logistic regression with fuzzy theory to construct the credit risk assessment model to increase the predicted accuracy of decision rate.