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Fraud Detection in Mobile Payments Utilizing Process Behavior Analysis

Generally, fraud risk implies any intentional deception made for financial gain. In this paper, we consider this risk in the field of services which support transactions with electronic money. Specifically, we apply a tool for predictive security analysis at runtime which observes process behavior with respect to transactions within a money transfer service and tries to match it with expected behavior given by a process model. We analyze deviations from the given behavior specification for anomalies that indicate a possible misuse of the service related to money laundering activities. We evaluate the applicability of the proposed approach and provide measurements on computational and recognition performance of the tool - Predictive Security Analyser - produced using real operational and simulated logs. The goal of the experiments is to detect misuse patterns reflecting a given money laundering scheme in synthetic process behavior based on properties captured from real world transaction events.