Research on the Periodical Behavior Discovery of Funds in Anti-money Laundering Investigation

Abstract

Some money laundering activities had periodic fund transfer behaviors, and discovering these cyclical behaviors was conducive to narrowing the scope of investigation. This paper treated the capital transaction data as a time series and found each periodic subsequence in the time series through the sub-period discovery algorithm, and designed the tolerance index to improve the robustness of the algorithm. In money laundering activities, there maight be linkage between related accounts. Through the relevant sub-period discovery algorithm, the highly correlated periodic behavior between different accounts were found, and then the suspicious accounts were found. A data set based on police investigation experience is constructed, and on this data set, the algorithm is validated to be effective.