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Fraud Detection in Mobile Payments Utilizing Process Behavior Analysis

Generally, fraud risk implies any intentional deception made for financial gain. This paper considers this risk in the field of services which support transactions with electronic money. Specifically, it applies 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. The paper analyzes deviations from the given behavior specification for anomalies that indicate a possible misuse of the service related to money laundering activities. The paper concludes by evaluating the applicability of the proposed approach and provide measurements on computational and recognition performance of the tool – Predictive Security Analyzer – 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.

Cleo Turner
Cleo is DFI's CDFP coach and helps our students with the apply section of the Certified Digital Finance Practitioner (CDFP) program. As part of her role Cleo shares useful resources and insights from a wide variety of sources and authors with our community.

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