Due to the popularity of cloud services it is unavoidable, that not just legitimate,
but fraudulent registrations will happen. For a service with good reputation it is
essential to prevent fraud users. A common way is to filter these cases during the
registration process by analysts. This paper presents a novel decision support system,
that can recognise anomalous behavioural patterns and classify accounts based on the
available data thus implementing an automated fraud prevention system. The process
uses both supervised and unsupervised approaches, thus avoiding errors due to inaccurate
labeling. As a supervised machine learning algorithm random forest classifier and
logistic regression, as an unsupervised auto encoder is used. The developed flow gives
a recommendation to the analyst whether a new user is potentially fraud or not and
provides feedback on the accuracy of analysts’ work based on the results of the unsupervised
approach. The newly developed process is able to supervise the decisions made by analysts
thus improving the labeling process. The main goal of this paper is to present a new,
more deterministic labeling workflow with the ability to provide feedback so it can
improve the correctness of the training data set.
