SMoTeF: Smurf money laundering detection using temporal order and flow analysis

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Abstract

Smurfing in financial networks is a popular fraud technique in which fraudsters inject their illegal money into the legitimate financial system. This activity is performed within a short period of time, with recurring transactions and multiple intermediaries. A major problem of existing graph-based methods for detecting smurfing is that they fall short of retrieving accurate fraud patterns. Consequently, the result is numerous non-fraudulent patterns alongside a few fraud patterns, causing a high false-positive rate. To alleviate this problem, we propose SMoTeF, a framework that extends existing graph-based smurf detection methods by distinguishing fraudulent smurfing patterns from non-fraudulent ones, thus significantly reducing the false-positive ratio. The core of the approach is a novel algorithm based on computing maximum temporal flow within temporal order of events. In order to evaluate the approach, a framework for injecting various smurfing patterns is developed, and experimental results on three real-world datasets from different domains show that SMoTeF significantly improves on the effectiveness of the state-of-the-art baseline, with only marginal runtime overhead.

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Shadrooh, S., & Nørvåg, K. (2024). SMoTeF: Smurf money laundering detection using temporal order and flow analysis. Applied Intelligence, 54(15–16), 7461–7478. https://doi.org/10.1007/s10489-024-05545-4

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