A novel statistical approach to detect card frauds using transaction patterns

Chae Chang Lee, Ji Won Yoon

    Research output: Contribution to journalArticlepeer-review

    Abstract

    In this paper, we present new methods for learning the individual patterns of a card user's transaction amount and the region in which he or she uses the card, for a given period, and for determining whether the specified transaction is allowable in accordance with these learned user transaction patterns. Then, we classify legitimate transactions and fraudulent transactions by setting thresholds based on the learned individual patterns.

    Original languageEnglish
    Pages (from-to)649-660
    Number of pages12
    JournalIEICE Transactions on Information and Systems
    VolumeE98D
    Issue number3
    DOIs
    Publication statusPublished - 2015 Mar 1

    Bibliographical note

    Publisher Copyright:
    Copyright © 2015 The Institute of Electronics, Information and Communication Engineers.

    Keywords

    • Association rule
    • Autoregressive
    • Fraud detection
    • Gaussian processes
    • Pattern mining

    ASJC Scopus subject areas

    • Software
    • Hardware and Architecture
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering
    • Artificial Intelligence

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