On the accuracy of decentralized virtual coordinate systems in adversarial networks
Proceedings of the 14th ACM conference on Computer and communications security
Off-the-peg and bespoke classifiers for fraud detection
Computational Statistics & Data Analysis
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Comprehensive study on methods of fraud prevention in credit card e-payment system
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Robust Decentralized Virtual Coordinate Systems in Adversarial Environments
ACM Transactions on Information and System Security (TISSEC)
Stock fraud detection using peer group analysis
Expert Systems with Applications: An International Journal
Security Through Collaboration and Trust in MANETs
Mobile Networks and Applications
An efficient algorithm for anomaly detection in a flight system using dynamic bayesian networks
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects, and then to detect any difference in evolution between the expected pattern and the target. The tool has been applied to the stock market data, which has been collected from Bangladesh Stock Exchange to assess its performance in stock fraud detection. We observed PGA can detect those brokers who suddenly start selling the stock in a different way to other brokers to whom they were previously similar. We also applied t-statistics to find the deviations effectively.