Investigation of anomalies in a RTC system using Machine Learning
Rafael Da Alesandro · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
In a Real Time Clearing System (RTCS) there are several thousands of transactions per second, and even more messages are sent back and forth. The high volume of messages and transactions being sent within the system eventually leads to some anomalies arising. This thesis examines how to detect such anomalies with unsupervised Machine Learning models such as, Support Vector Machine(SVM) One Class (OC), Isolation Forest (iForest) and Local Outlier Factor(LOF). The main objective is to investigate if anomaly detection is useable in Cinnobers RTCS, only using unsupervised models and if they perform at an acceptable level. The evaluation of the models will be done using a rough labeling method to score them on detection rate, F-score and Mahews correlation coecient (MCC). The results of the thesis shows that SVM OC is the best model of the three, but requires hyper parameter tuning to perform at an acceptab lelevel so that it may be used for the RTCS without human supervision.