Credit Risk Models for Consumer Banking

• Main Authors: Ju-Chen Cheng, 鄭如珍
• Other Authors: Yi-Chuan Lu
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/53073591460116383963

碩士 === 元智大學 === 資訊管理學系 === 94 === Recently, the business focus of banking industry has been shifting from corporate banking to consumer banking because of the high profit and high interest spread. Due to the relatively small market scale, the consumer banking business is indeed a highly competitive business in Taiwan. From the risk management’s point of view, the purpose of this research is to build up a credit risks model for consumer banking. By building the model, the bank could evaluate the underlying risk exposures on its consumer banking business so as to come out strategies to decrease the probabilities of bad accounts and reduce the operating costs, eventually, to maximize the profits. In the thesis, we first collect customer information and their transaction records to form a research database. Several algorithms, known as data mining techniques, are applied to build up the credit risk models. The algorithms are CART, Back-propagation Neural Network, Logistic Regression and K-Mean Clustering. The critical success factors for building up a reliable and robust model are discussed in this thesis as well.