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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