Fraudulent Electronic Transaction Detection Using Dynamic KDA Model

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.

Provided by: Cornell University Topic: Data Management Date Added: Feb 2015 Format: PDF

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