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Data Mining for Secure Online Payment Transaction
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Author(s): Masoumeh Zareapoor (Shanghai Jiao Tong University, China), Pourya Shamsolmoali (Advanced Scientific Computing, CMCC, Italy)and M. Afshar Alam (Jamia Hamdard University, India)
Copyright: 2017
Pages: 28
Source title:
Handbook of Research on Advanced Data Mining Techniques and Applications for Business Intelligence
Source Author(s)/Editor(s): Shrawan Kumar Trivedi (BML Munjal University, India), Shubhamoy Dey (Indian Institute of Management, Indore, India), Anil Kumar (BML Munjal University, India)and Tapan Kumar Panda (Jindal Global Business School, India)
DOI: 10.4018/978-1-5225-2031-3.ch005
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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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