A Comprehensive Survey of Data Mining-based Fraud Detection Research
Year: 2010
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Phua, Clifton, Lee, Vincent, Smith, Kate, Gayler, Ross
Institution: Monash University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6929467385