Anti-money-laundering: how graph AI exposes what rules-based systems miss
Rules catch known patterns. Graphs catch the structures criminals invent to evade the rules.
Traditional AML relies on threshold rules and watchlists. Sophisticated laundering deliberately stays under thresholds and uses layered entities to break the audit trail. Graph-based AI reconstructs that trail by connecting entities, transactions and ownership across sources. Combined with balance-sheet analysis, it surfaces laundering typologies that per-transaction monitoring cannot.
Signals only a graph reveals
- Layered ownership designed to obscure ultimate beneficiaries
- Rapid entity creation and dissolution around the same actors
- Balance sheets inconsistent with declared activity
- Networks of shared addresses, directors and intermediaries
Shell-company detection
Balance-sheet analysis combined with graph context flags entities whose financials do not match their stated activity, auto-preparing an investigation file.
Secured authority reporting
Confirmed signals generate structured reports to financial-intelligence units through secured APIs, with a complete audit trail.