Fraudulent Electronic transaction detection using KDA Model

2015/03/11 by M. Vadoodparast, Vadoodparast, M., Abdul Razak Hamdan +3
Computer Science · #Computers and Society (cs.CY) #Databases (cs.DB) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #cs.CY #cs.DB

paper · pdf · doi:10.48550/arxiv.1503.03208

arxiv created 2015/03/11 · openalex publication_date 2015/03/11 · arxiv updated 2015/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Clustering analysis and Datamining 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 Rapidminer modeling tool. The research objectives are 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 propose a good supporting procedure to detect fraudulent transaction dynamically.

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