A novel statistical approach to detect card frauds using transaction patterns

Chae Chang Lee; Ji Won Yoon

doi:10.1587/transinf.2014EDP7071

A novel statistical approach to detect card frauds using transaction patterns

Chae Chang Lee, Ji Won Yoon

School of Cybersecurity

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	649-660
Number of pages	12
Journal	IEICE Transactions on Information and Systems
Volume	E98D
Issue number	3
DOIs	https://doi.org/10.1587/transinf.2014EDP7071
Publication status	Published - 2015 Mar 1

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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10.1587/transinf.2014EDP7071

Cite this

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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.",

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A novel statistical approach to detect card frauds using transaction patterns

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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