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Survey of Clustering based Financial Fraud Detection Research

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  • Andrei Sorin SABAU

Abstract

Given the current global economic context, increasing efforts are being made to both prevent and detect fraud. This is a natural response to the ascendant trend in fraud activities recorded in the last couple of years, with a 13% increase only in 2011. Due to ever increasing volumes of data needed to be analyzed, data mining methods and techniques are being used more and more often. One domain data mining can excel at, suspicious transaction monitoring, has emerged for the first time as the most effective fraud detection method in 2011. Out of the available data mining techniques, clustering has proven itself a constant applied solution for detecting fraud. This paper surveys clustering techniques used in fraud detection over the last ten years, shortly reviewing each one.

Suggested Citation

  • Andrei Sorin SABAU, 2012. "Survey of Clustering based Financial Fraud Detection Research," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 16(1), pages 110-122.
    • Handle: RePEc:aes:infoec:v:16:y:2012:i:1:p:110-122
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    Cited by:

    1. Prashant Priyadarshi & Prabhat Kumar, 2024. "A comprehensive review on insider trading detection using artificial intelligence," Journal of Computational Social Science, Springer, vol. 7(2), pages 1645-1664, October.
    2. Jennifer J. Xu, 2016. "Are blockchains immune to all malicious attacks?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-9, December.

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