Risk Prediction of Credit Loan Using Backpropagation Nwural Network

• Main Authors: Chi-Jou Young, 楊啟洲
• Other Authors: Heng Ma
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/28503255685667719504

碩士 === 中華大學 === 科技管理研究所 === 93 === According to the Bureau of Monetary Affairs, Financial Supervisory Commission in Taiwan, as of the end of December 2001, the average of nonperforming loan ratio of all banks had reached a historical high. As the financial leverage effect of the banks is getting worse, and the rate of interest is decreasing, the interest collected can no more cover the loss caused by the bad debts. Credit guaranty has becomes one of the important means, under the severe competition nowadays, to avoid as well as predict the risk of loan. Traditionally approaches using statistic or mathematic model to accomplish the risk-avoiding task, such as discriminant analysis and logistic regression have limited themselves to a stricter environment or background, which is lack of adaptability in reality. In this paper, a neural network trained by the backpropagation paradigm (BPN) is utilized as a tool for predicting the risk in credit guaranty. We enumerate 37 discriminated variables, partly theoretical and empirical, as the input variables for the neural network. The data were collected from a financial institute, where those between 1999 and 2002 were used for training and between 2003 and 2005 were used for testing. As a result, The BPN achieved a correct prediction rate of nearly 100% in predicting the attribute of the loaner. The proposed model is suitable for a decision-support tool in granting loans; furthermore, it establishes the groundwork for value-created activities in the customer relation management.