Electronic Banking Fraud Detecting Publish Msc Thesis

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 Technique, Machine Learning and Predictive modeling for Unsupervised Anomaly Detection Algorithms on Electronic banking transaction dataset record for over a period of six (6) months, April to September, 2015 consisting of 9 variable data fields and 8,641 observations was used to carry out the survey on fraud detection. On completion of the underlying system, I can conclude that integrated techniques systems 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 unlabeled data set used for this research work, clustering based method is preferred to classification model.