Electronic Banking Fraud Detection. Using Data Mining Techniques And R Software For Implementing Machine Learning Algorithms In Prevention Of Fraud

      Аннотация: 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.

Ключевые слова: Clustering Analysis, data analysis, Data Management, Data Mining, DATA MODELLING, Data Visualisation, Electronic Banking, fraud detection, Inferential Statistics, mathematical statistics, PCA, Statistical modelling, Statistical Application Software