Anti-Money Laundering
Unmask Money Laundering Networks
Rocketgraph traces complex money flows across billions of transactions in real time.
Anti-Money Laundering
Unmask Money Laundering Networks
Rocketgraph traces complex money flows across billions of transactions in real time.
The Complexity of Money Laundering
Money laundering operations thrive on complexity: splitting funds, layering transactions across accounts and countries, then reconverging them into seemingly legitimate flows. Detecting these patterns is one of the most pressing compliance challenges for financial institutions today.
Traditional systems struggle with joins and siloed datasets. By the time suspicious activity is pieced together, regulators are already demanding answers.
Full Pie Analysis, not Just a Slice
Money laundering is about following the money. Graph technology connects customers, accounts, and transactions into one continuous network. Compliance teams can follow the trail across multiple hops, intermediaries, and jurisdictions.
This approach reveals:
- Layering schemes designed to obscure origins
- Round-robin transfers through shell accounts
- Common identifiers (emails, IPs) hidden across accounts
Query Speed is King
Other graph solutions can visualize portions of these networks, but they slow to a standstill after the dataset reaches a certain size.
Rocketgraph is different
Rocketgraph ensures no laundering scheme escapes detection — no matter how complex.
Optimized for Scale
Analyzes billions of transactions in real time.
Full-Network Visibility
Traces laundering schemes across entire ecosystems.
Fast Investigations
Delivers context instantly, reducing analyst workload.
Eliminate your Risk
Rocketgraph delivers:
- Faster suspicious activity reporting that meets regulatory deadlines.
- Reduced risk of fines by ensuring complete investigations.
- Fewer false positives, allowing compliance teams to focus on real threats.
Bottom line:
Rocketgraph transforms AML from a compliance burden into a competitive advantage.
FAQ
Rocketgraph models entities and relationships as a property graph and supports graph-wide, multi-hop pattern search well-suited to AML where criminals move funds across seemingly unrelated accounts.
Rocketgraph’s high-performance, in-memory (scale-up) graph engine is designed to lower latency on complex, many-hop queries across very large graphs. The result is “warp-speed” graph-wide scanning, with multi-day graph jobs dropping to hours. Your actual performance may depend on data volume, hardware, and query complexity.
Rocketgraph provides a GenAI-assisted “Mission Control” so analysts can express intents in natural language; the system generates executable graph models/queries, visualizes results, and supports iterative, explainable analysis—reducing dependence on specialist query languages.
The platform takes a property-graph approach and zero-ETL connectors (cloud or on-prem deployment), with evaluation criteria around ease of data integration and schema creation. Please inquire about your specific sources and throughput.
By searching for subgraph patterns and traversing deep paths with graph-wide scanning, investigators can move from isolated alerts to network-context case building e.g., proximity to known bad actors, multi-hop flows, and intermediary roles while using explainable, iterative outputs for documentation. The depth-first search capability supports deep path exploration during investigations.
FAQ
Rocketgraph models entities and relationships as a property graph and supports graph-wide, multi-hop pattern search well-suited to AML where criminals move funds across seemingly unrelated accounts.
Rocketgraph’s high-performance, in-memory (scale-up) graph engine is designed to lower latency on complex, many-hop queries across very large graphs. The result is “warp-speed” graph-wide scanning, with multi-day graph jobs dropping to hours. Your actual performance may depend on data volume, hardware, and query complexity.
Rocketgraph provides a GenAI-assisted “Mission Control” so analysts can express intents in natural language; the system generates executable graph models/queries, visualizes results, and supports iterative, explainable analysis—reducing dependence on specialist query languages.
The platform takes a property-graph approach and zero-ETL connectors (cloud or on-prem deployment), with evaluation criteria around ease of data integration and schema creation. Please inquire about your specific sources and throughput.
By searching for subgraph patterns and traversing deep paths with graph-wide scanning, investigators can move from isolated alerts to network-context case building e.g., proximity to known bad actors, multi-hop flows, and intermediary roles while using explainable, iterative outputs for documentation. The depth-first search capability supports deep path exploration during investigations.
