Application of Personlized Approaches to Detection of Credit Card Fraud

• Main Authors: Luo-Shu-TIng, 羅書婷
• Other Authors: Rong-Chang Chen
• Format: Others
• Language: zh-TW
• Published: 2006
• Online Access: http://ndltd.ncl.edu.tw/handle/20253784120594360626

碩士 === 國立臺中技術學院 === 事業經營研究所 === 94 === Credit cards are a popular tool for transactions in many countries lately. However, credit card frauds have occurred frequently. How to detect credit card frauds, therefore, has become a key issue in recent years. Many previous studies proposed models which were constructed from the past real transaction data of many others to detect new transactions of a certain individual. In contrast to those traditional approaches, this study employs a personalized approach to solve the problem of credit card fraud. The personalized approach proposes to prevent fraud before the consumer uses a credit card or when the collected data are few. This new approach is promising. However, there are still some problems which have to be solved. For examples, 1) consumers are not willing to spend too much time to answer questions so that the collected data are few, 2) the dynamic consumer behavior may cause data overlapping. To improve the problems mentioned above, this research employs the personalized approach to address the credit card fraud problem. The main purpose of this study is to investigate the influences of data distribution on the prediction accuracy. Support vector machine (SVM), back propagation network (BPN), and binary support vector system (BSVS) are used to construct detection models for credit card fraud. The experimental results show that SVM and BPN can obtain good training results. However, both techniques fail to predict future data accurately for those cases with high training results. Besides, the classification results of these three classifiers are comparable. Compared to the other two techniques, BSVS is the easiest tool to use. This study also employs several techniques, such as hierarchical SVM, majority voting, and over-sampling, to improve true negative rates. Results from the experiments indicate that these techniques can increase true negative rates effectively.