Rudra
Trace.
Money laundering is a network problem. Rudra Trace is built on a Neo4j knowledge graph, retrieval-augmented adaptive rules, agentic triage, and human-in-the-loop governance. Already running in a regulated cross-border payments environment.
Rule engines were built for a different problem.
Industry-standard transaction monitoring sits at 95–98% false-positive rates. Compliance teams spend their time clearing noise instead of investigating risk. Static thresholds cannot adapt to geopolitical events, emerging typologies, or shifting sanctions landscapes.
Flat-file architectures miss the network patterns, such as circular flows, fan-out distributions, and structuring across corridors, that are the actual shape of financial crime.
Money laundering is a network problem. Only a graph-native system can see it.
Five layers. One continuous audit trail.
Every layer feeds the next; every decision is defensible to a regulator.
The graph builds itself.
Transactions stream into a Neo4j knowledge graph in real time. Identity resolution consolidates millions of fragmented KYC records into unified master identities. Investigators see the whole network behind an alert, not isolated rows.
Rules that update themselves.
A retrieval-augmented layer consumes new FATF typologies, updated sanctions lists, and geopolitical intelligence. Detection thresholds adjust in real time, tightening scrutiny on high-risk entities and suppressing noise on low-risk corridors, with no manual rule rewrites required.
Cases investigated at machine speed.
LLM-powered agents classify alerts, generate Requests for Information, draft narrative summaries, and pre-populate Suspicious Activity Reports with full evidentiary chains. Every decision carries a complete audit trail of reasoning, data sources, and confidence scores.
No regulator-facing action without a human.
Compliance officers review LLM-assisted classifications, approve or override triage outcomes, and validate SAR narratives. Regulatory accountability is architectural, enforced by the system rather than by a process document.
From alert genesis to filing, in one trail.
Automated PDF report generation, direct API integration with regulatory filing systems, and structured audit logs. Full traceability from the moment an alert fires to the moment a regulator receives the filing.
Graph-native from day one.
Rudra Trace has been graph-native from its first release. Where others add graph capability to a relational core, we began with the graph, because money laundering is a network problem and only a graph-native system can see it.
Measured at a regulated launch partner.
Operational improvements measured in a live cross-border payments environment. Baselines are held in confidence, so we show the shift rather than the underlying figures. Results vary by environment.
Built for cross-border payments. The graph pattern travels.
AML and sanctions for payments.
Cross-border and remittance networks, interbank clearing operators, acquirer-processors, mobile money operators. The original use case, and where the production deployment lives.
Regulated neobanks and card networks.
Adjacent regulated finance where graph-pattern detection unlocks better triage than rule engines: scaling neobanks, card networks, crypto on/off-ramps with KYC obligations, trade finance.
Sanctions screening beyond banking.
Maritime cargo and chartering (OFAC sanctions on vessels and owners), commodities trading, shipping insurance, and trade finance. Same graph problem, different regulator.
Fraud ring and network detection.
Healthcare claims fraud, insurance fraud, telecom fraud (SIM swap rings, IRSF), ad-tech and marketplace collusion, tax-authority anti-fraud. Network-shaped problems wherever they appear.
Talk to the team behind
Rudra Trace.
In production with a launch partner. Selectively onboarding additional regulated environments. Briefings available for compliance leadership.