Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling
Money launderers take advantage of limitations in existing detection approaches by hiding their financial footprints in a deceitful manner. They manage this by replicating transaction patterns that the monitoring systems cannot easily distinguish. As a result, criminally gained assets are pushed into legitimate financial channels without drawing attention. Algorithms developed to monitor money flows often struggle with scale and complexity. The difficulty of identifying such activities is further intensified by the (persistent) inability of current solutions to control the excessive number of false positive signals produced by rigid, risk-based rules systems. We propose a framework called...
| Authors | Haseeb Tariq, Alen Kaja, Marwan Hassani |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 14 April 2026 |
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