The base-rate fallacy and the difficulty of intrusion detection
ACM Transactions on Information and System Security (TISSEC)
Distributed Data Mining in Credit Card Fraud Detection
IEEE Intelligent Systems
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection
EEE '04 Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'04)
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Dempster-Shafer Theory for Intrusion Detection in Ad Hoc Networks
IEEE Internet Computing
Securing credit card transactions with one-time payment scheme
Electronic Commerce Research and Applications
A game-theoretic approach to credit card fraud detection
ICISS'05 Proceedings of the First international conference on Information Systems Security
Neural fraud detection in credit card operations
IEEE Transactions on Neural Networks
Detecting credit card fraud by genetic algorithm and scatter search
Expert Systems with Applications: An International Journal
Singular sources mining using evidential conflict analysis
International Journal of Approximate Reasoning
Nearest-neighbor-based approach to time-series classification
Decision Support Systems
Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster-Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster-Shafer's theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.