Detecting Anomalies in Credit Card Transaction Using Efficient Techniques

Divya Jennifer DSouza () and Venisha Maria Tellis
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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

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 161-171 from Springer

Abstract: 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.

Keywords: Anomaly detection; K-means; Support vector machine; Clustering (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_15

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DOI: 10.1007/978-3-030-41862-5_15

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