Computer Science – Artificial Intelligence
Scientific paper
2010-09-30
Computer Science
Artificial Intelligence
14 pages
Scientific paper
10.1016/j.chb.2012.01.002
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.
Gayler Ross
Lee Vincent
Phua Clifton
Smith Kate
No associations
LandOfFree
A Comprehensive Survey of Data Mining-based Fraud Detection Research does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with A Comprehensive Survey of Data Mining-based Fraud Detection Research, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Comprehensive Survey of Data Mining-based Fraud Detection Research will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-243203