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Detecting Anomalies in Credit Card Transaction Using Efficient Techniques

In: New Trends in Computational Vision and Bio-inspired Computing

Author

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  • Divya Jennifer DSouza

    (NMAM Institute of Technology, Department of Computer Science and Engineering)

  • Venisha Maria Tellis

    (NMAM Institute of Technology, Department of Computer Science and Engineering)

Abstract

Now a days detecting anomalies has become wide domain and it is considered as one of the main problem in many applications. From the standard, normal, or expected behavior something that varies from these are called as anomalies. Anomaly detection is identifying or finding anomalies from various applications. There are some kind of problems that arises in many applications such as structural defects, frauds or errors, the anomalous items have the potential of getting converted into such problems. Many techniques or methods are developed and are used for detecting anomalies. In this paper implementation using K-means, Support vector machine techniques for detecting anomalies in credit card transaction dataset have been described and accuracy is calculated to determine which technique is efficient in detecting anomalies.

Suggested Citation

  • Divya Jennifer DSouza & Venisha Maria Tellis, 2020. "Detecting Anomalies in Credit Card Transaction Using Efficient Techniques," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 161-171, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_15
    DOI: 10.1007/978-3-030-41862-5_15
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