Data Mining for Credit Scoring

Data Mining for Credit Scoring

Indranil Bose (University of Hong Kong, China), Cheng Pui Kan (University of Hong Kong, China), Chi King Tsz (University of Hong Kong, China), Lau Wai Ki (University of Hong Kong, China), and Wong Cho Hung (University of Hong Kong, China)
DOI: 10.4018/978-1-59904-951-9.ch148
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Credit scoring is one of the most popular uses of data mining in the financial industry. Credit scoring can be defined as a technique that helps creditors decide whether to grant credit to customers. With the use of credit scoring decisions about granting of loans can be made in an automated and faster way in order to assist the creditors in managing credit risk. This chapter begins with an explanation of the need for credit scoring followed by the history of credit scoring. Then it discusses the relationship between credit scoring and data mining. The major applications of credit scoring in three areas, which include credit card, mortgage and small business lending, are introduced. This is followed by a discussion of the models used for credit scoring and evaluation of seven major data mining techniques for credit scoring. A study of default probability estimation is also presented. Finally the chapter investigates the benefits and limitations of credit scoring as well as the future developments in this area.

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