Fraudulent Electronic Transaction Detection Using Dynamic KDA Model

Provided by: Cornell University
Topic: Data Management
Format: PDF
Clustering analysis and data mining methodologies were applied to the problem of identifying illegal and fraud transactions. The researchers independently developed model and software using data provided by a bank and using rapid-miner modeling tool. This paper is to propose dynamic model and mechanism to cover fraud detection system limitations. KDA model as proposed model can detect 68.75% of fraudulent transactions with online dynamic modeling and 81.25% in offline mode and the fraud detection system & decision support system. Software proposes a good supporting procedure to detect fraudulent transaction dynamically.

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