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CORR
2010
Springer

A Comprehensive Survey of Data Mining-based Fraud Detection Research

13 years 2 months ago
A Comprehensive Survey of Data Mining-based Fraud Detection Research
This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains. Keywords Data mining applications, automated fraud detection, adversarial detection
Clifton Phua, Vincent C. S. Lee, Kate Smith-Miles,
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2010
Where CORR
Authors Clifton Phua, Vincent C. S. Lee, Kate Smith-Miles, Ross Gayler
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