Electronic Banking Fraud Detection

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.

Autorentext

Enoch Sayo Aluko, a CIE Examiner and Assessment Specialist attended University of Lagos, where he obtained B.Sc, in Education Mathematics and M.Sc., in Statistics. Besides, he has Diploma in Data Mining (SIIT) and a Certificate Course in Data Management and Visualization (Wesleyan University). He is a member of the Nigeria Mathematical Society.