Outlier Detection with Explanation Facility
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Outlier Detection with a Hybrid Artificial Intelligence Method
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Data mining for credit card fraud: A comparative study
Decision Support Systems
Outlier analysis for plastic card fraud detection a hybridized and multi-objective approach
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Mining recent temporal patterns for event detection in multivariate time series data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised fraud detection in Medicare Australia
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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The Internet has taken its place beside the telephone and the television as an important part of people's lives. Consumers rely on the Internet to shop, bank and invest online. Most online shoppers use credit cards to pay for their purchases. As credit card becomes the most popular mode of payment, cases of fraud associated with it are also increasing. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is trained with normal behavior of cardholder. If an incoming credit card transaction is not accepted by the HMM with sufficiently high probability, it is considered to be fraudulent. We present detailed experimental results to show the effectiveness of our approach.