The Design of Credit Risk-Based Data mart - A Case Study of Consumer Banking Management

• Main Authors: LIU,EN-JUNG, 劉恩榮
• Other Authors: 洪大為
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/07185587489285144218

碩士 === 實踐大學 === 企業創新發展研究所 === 94 === The meaning of credit risk usually relates to the risks of credit loans, and the loss to a bank brought by the inability of the borrower to repay his loan promptly. The overall meaning of credit risk, however, points to all credit risks relevant to default of contract. According to research by the Bank for International Settlements, credit risk is most faced by financial institutes, accounting for approximately 60% of all risks, followed by operational risks, 30%, and market risk and other risks are faced least, accounting for about 5%. Therefore, it is significant that banks should focus more on the control of credit and operational risk in the future. The contents relevant to credit risk in the BASEL II Accord should include 1) loss for the inability for the borrower to reimburse his loan, causing the change in value of creditor’s rights or when an increase in cost for funding changes credit ratings. 2) Contract default is risks from financial derivative product transaction default, its credit risk include replacement costs from the original contract, and extra costs for underlying risks of the derivatives in the future. As for banks, credit evaluation for borrower previously applies 5C, 3F and 5P methods, and does not quantify necessary data (such as amount of risk, anticipated risks quantification). In the integration and application of bank data warehouse, most often the data warehouse and data mart are used to apply to different strategic needs in each department or organization, and it provides related information analysis for the user. In the BASEL II Accord, in contrast to data marts for marketing and sales purposes, banks need a data mart especially for management of risks, to comply with the Accord and evaluation of various information and risks, to effectively support strategic analysis. This research focuses on data mart that supports credit risk management. Banks that handles consumer finances will be discussed, as well as the method for constructing a data mart credit ratings for borrowers, and for calculation of losses in contract default, to assist banks in effective management and to reduce amount of losses.