Introducing a Method for Combining Supervised and Semi-Supervised Methods in Fraud Detection

As electronic transactions are growing, fraud cases are also growing drastically. Detection of frauds is a complicated task and limiting fraud detection systems to certain kinds of detection methods like supervised or unsupervised methods does not seem efficient. In this paper, a combination framework for fraud detection systems, consisting of both supervised and semi-supervised methods in three main components namely rule-based component, trend-analysis-based component and, a scenario-based component is proposed. The rule-based component is the supervised part of the framework and decision tree which is a cost-insensitive classification algorithm is used for this component. In the trend-analysis-based component, which is the semi-supervised part of our proposed framework, the normal behavior of users are modeled and the extent of dissimilarities of newly-arrived transactions are calculated. Finally, in the scenario-based component which is another semi-supervised part of the proposed framework, the extent of similarities of the sequence of transactions to known fraud scenarios are calculated. The final result is gained through combining the results of all these three components using in a parallel mode. By combining the outputs of all these components together using the SUM function, the detection rate has increased remarkably (about 7%).