Conditional Weighted Transaction Aggregation for Credit Card Fraud Detection (original) (raw)

Abstract

Credit card fraud causes substantial losses to credit card companies and consumers. Consequently, it is important to develop sophisticated and robust fraud detection techniques that can recognize the subtle differences between fraudulent and legitimate transactions. Current fraud detection techniques mainly operate at the transaction level or account level. However, neither strategy is foolproof against fraud, leaving room for alternative techniques and improvements to existing techniques. Transaction-level approaches typically involve the analysis and aggregation of previous transaction data to detect credit card fraud. However, these approaches usually consider all the transaction attributes to be equally important. The conditional weighted transaction aggregation technique described in this paper addresses this issue by leveraging supervised machine learning techniques to identify fraudulent transactions. Empirical comparisons with existing transaction level methods and other transaction aggregation based methods demonstrate the effectiveness of the proposed technique.

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Authors and Affiliations

  1. Cybercrime and Security Intelligence Department, Institute for Infocomm Research, Singapore, Singapore
    Wee-Yong Lim, Amit Sachan & Vrizlynn Thing

Authors

  1. Wee-Yong Lim
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  2. Amit Sachan
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  3. Vrizlynn Thing
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Editors and Affiliations

  1. Air Force Institute of Technology, Wright-Patterson Air Force Base, 45433-7765, OH, USA
    Gilbert Peterson
  2. University of Tulsa, 74104-3189, Tulsa, OK, USA
    Sujeet Shenoi

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Lim, WY., Sachan, A., Thing, V. (2014). Conditional Weighted Transaction Aggregation for Credit Card Fraud Detection. In: Peterson, G., Shenoi, S. (eds) Advances in Digital Forensics X. DigitalForensics 2014. IFIP Advances in Information and Communication Technology, vol 433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44952-3\_1

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