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Detection Mechanism of Money Laundering based on Random Walk and Skip-Grim Model

Money laundering means that criminals use the services provided by banks to transfer a large amount of illegal funds to untraceable destination accounts. Most of the related works are rule-based and machine learning based anti-money laundering systems. However, the anti-money laundering systems based on machine learning are affected by the data scale of money laundering transactions. The rule-based anti-money laundering systems require a lot of manual work and cannot adapt to the changing money laundering behavior. Therefore, this paper designs a money laundering detection mechanism based on random walk and skip-grim model. This detection mechanism preferentially constructs an account transfer graph. Then, on this basis, it generates random transfer trajectories for each account using the random walk algorithm. Thereafter, the transfer characteristics of each user are automatically analyzed from the random transfer trajectories utilizing the skip-grim model. Finally, it compares the extracted transfer characteristics of different users and combines the cosine similarity to identify illegal money laundering users. Last, we use python programming language and cbank dataset to evaluate the performance of the proposed scheme, and we compare our work with the related works, flowscope and Martin jullum. The extensive simulation results validate that, the proposed scheme has better identification effect, correctly identifying more than 80% of the money laundering accounts, and the misjudgment rate is less than 20 %.