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Electronic Banking Fraud Detection: Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud
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Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!
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| This research work deals with the procedures for computing the presence of outliers using various distance measures and general detection performance for unsupervised machine learning, such as the K-Mean Clustering Analysis and Principal Component Analysis. A comprehensive evaluation of Data Mining Techniques, Machine Learning and Predictive modelling for Unsupervised Anomaly Detection Algorithms on Electronic Banking Transaction data sets record for over a period of six (6) months, April to September, 2015, consisting of 9 variable data fields and 8,641 observations, were used to carry out the survey on fraud detection. On completion of the underlying system, I can conclude that integrated techniques system provide better performance efficiency than a singular system. Besides, in near real-time settings, if a faster computation is required for larger data sets, just like the unlabelled data sets used for this research work, clustering based method is preferred to classification model. |
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