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Hybridization of SOM and PSO for Detecting Fraud in Credit Card

Hybridization of SOM and PSO for Detecting Fraud in Credit Card

Suman Arora, Dharminder Kumar
Copyright: © 2017 |Volume: 9 |Issue: 3 |Pages: 20
ISSN: 1935-5688|EISSN: 1935-5696|EISBN13: 9781522511854|DOI: 10.4018/IJISSS.2017070102
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MLA

Arora, Suman, and Dharminder Kumar. "Hybridization of SOM and PSO for Detecting Fraud in Credit Card." IJISSS vol.9, no.3 2017: pp.17-36. http://doi.org/10.4018/IJISSS.2017070102

APA

Arora, S. & Kumar, D. (2017). Hybridization of SOM and PSO for Detecting Fraud in Credit Card. International Journal of Information Systems in the Service Sector (IJISSS), 9(3), 17-36. http://doi.org/10.4018/IJISSS.2017070102

Chicago

Arora, Suman, and Dharminder Kumar. "Hybridization of SOM and PSO for Detecting Fraud in Credit Card," International Journal of Information Systems in the Service Sector (IJISSS) 9, no.3: 17-36. http://doi.org/10.4018/IJISSS.2017070102

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Abstract

Fraud Detection is a detection of criminal activity that generally occurs in commercial organization. Detection of such fraud can prevent a great economic loss. Credit card fraud depends upon usage of card, its unusual transactions behavior or any unauthorized activity on a credit card. Clustering process can divide the data into subsets and it can be very helpful in credit card fraud detection where outlier may be more interesting than common cases. Self-organizing Map (SOM) is unsupervised clustering technique which is very efficient and handling large and high dimensional dataset. Particle Swarm Optimization (PSO) is another stochastic optimization technique based on intelligent of swarms. In the present study, we combine these two methods and present a new hybrid approach self-organizing Particle Swarm Optimization (SOPSO) in detection of credit card fraud. In order to apply our method, we demonstrated an example and its results are compared with previous techniques. Some challenges shown in the previous researches such as time and space complexity, false positive rate and supervised techniques. Our approach is efficient as it implements one of the optimization technique and unsupervised approach which results in less time and space complexity and false positive rate is very low. Domain independency is also achieved in our approach.

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