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Data Mining for Secure Online Payment Transaction

Data Mining for Secure Online Payment Transaction

Masoumeh Zareapoor, Pourya Shamsolmoali, M. Afshar Alam
Copyright: © 2019 |Pages: 27
ISBN13: 9781522562016|ISBN10: 152256201X|EISBN13: 9781522562023
DOI: 10.4018/978-1-5225-6201-6.ch016
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MLA

Zareapoor, Masoumeh, et al. "Data Mining for Secure Online Payment Transaction." Digital Currency: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2019, pp. 286-312. https://doi.org/10.4018/978-1-5225-6201-6.ch016

APA

Zareapoor, M., Shamsolmoali, P., & Alam, M. A. (2019). Data Mining for Secure Online Payment Transaction. In I. Management Association (Ed.), Digital Currency: Breakthroughs in Research and Practice (pp. 286-312). IGI Global. https://doi.org/10.4018/978-1-5225-6201-6.ch016

Chicago

Zareapoor, Masoumeh, Pourya Shamsolmoali, and M. Afshar Alam. "Data Mining for Secure Online Payment Transaction." In Digital Currency: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 286-312. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-6201-6.ch016

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Abstract

The fraud detection method requires a holistic approach where the objective is to correctly classify the transactions as legitimate or fraudulent. The existing methods give importance to detect all fraudulent transactions since it results in money loss. For this most of the time, they have to compromise on some genuine transactions. Thus, the major issue that the credit card fraud detection systems face today is that a significant percentage of transactions labelled as fraudulent are in fact legitimate. These “false alarms” delay the transactions and creates inconvenience and dissatisfaction to the customer. Thus, the objective of this research is to develop an intelligent data mining based fraud detection system for secure online payment transaction system. The performance evaluation of the proposed model is done on real credit card dataset and it is found that the proposed model has high fraud detection rate and less false alarm rate than other state-of-the-art classifiers.

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