Abstract
This work proposes a new method to solve the credit card fraud problem. Traditionally, systems based on previous transaction data were set up to predict a new transaction. This approach provides a good solution in some situations. However, there are still many problems waiting to be solved, such as skewed data distribution, too many overlapped data, fickle-minded consumer behavior, and so on. To improve the above problems, we propose to develop a personalized system, which can prevent fraud from the initial use of credit cards. First, the questionnaire-responded transaction (QRT) data of users are collected by using an online questionnaire based on consumer behavior surveys. The data are then trained by using the support vector machines (SVMs) whereby the QRT models are developed. The QRT models are used to predict a new transaction. Results from this study show that the proposed method can effectively detect the credit card fraud.
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Chen, RC., Chiu, ML., Huang, YL., Chen, LT. (2004). Detecting Credit Card Fraud by Using Questionnaire-Responded Transaction Model Based on Support Vector Machines. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_119
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DOI: https://doi.org/10.1007/978-3-540-28651-6_119
Publisher Name: Springer, Berlin, Heidelberg
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