Fraud detection
Stop Fraud Before It Spreads
Rocketgraph uncovers fraud rings hidden across billions of connections — in seconds, not hours.
Fraud detection
Stop Fraud Before It Spreads
Rocketgraph uncovers fraud rings hidden across billions of connections — in seconds, not hours.
Unified Data Fraud Detection
Fraud today is rarely a single suspicious transaction. It’s networks of accounts, devices, and identities working together to hide in plain sight. Fraudsters create complex webs of connections that look legitimate on the surface but reveal coordinated abuse when examined in context.
Traditional tools struggle with this scale and complexity. Relational databases choke on multi-hop joins, and rule-based systems generate floods of false positives. By the time suspicious activity surfaces, it’s already too late.
Executives need a faster, smarter way to reveal the hidden structure of fraud.
Full Pie Analysis, not Just a Slice
Fraud is about connections. Graph technology models every account, transaction, and identifier as part of a living network. Instead of asking “what happened here,” you can ask “how is this connected?”
This approach makes complex fraud patterns visible:
- Circular money flows across accounts
- Shared devices or phone numbers behind multiple identities
- Long transaction chains leading to a known bad actor
With graph analytics, fraud detection becomes about seeing the full picture — not just isolated data points.
Find the Needle in a Haystack, In a Field of Haystacks
Not all graph solutions are created equal. Many vendors can capture fraud at a surface level, but slow down when queries require scanning entire networks many layers deep.
Rocketgraph is different
Rocketgraph gives your fraud teams the ability to go beyond shallow alerts and uncover coordinated rings that others miss.
Patented Graph Search Optimization
Uncovers fraud hidden 10, 20, or more hops away — in seconds.
Enterprise Scale
Handles billions of nodes and edges without bottlenecks.
Real-Time Response
Continuously ingests transactions and flags suspicious activity as it happens.
Ease of Integration
Works with your existing fraud systems, enriching them with deeper insights.
Eliminate your Risk
Rocketgraph delivers:
- Faster detection of fraud before it spreads.
- Reduced false positives, lowering investigation costs.
- Comprehensive coverage, so no network-wide pattern is missed.
- Operational confidence, knowing your fraud defenses scale with the threat.
Bottom line:
Rocketgraph transforms fraud detection from reactive alert‑chasing into proactive risk elimination.
FAQ
Graph analytics is designed to reveal relationships and hidden pathways traditional methods overlook. Rocketgraph models entities and connections as a property graph and supports graph-wide, multi-hop pattern search, which is directly applicable to anti-money-laundering and fraud ring discovery.
Rocketgraph’s xGT engine applies high-performance computing (HPC) principles (in-memory, scale-up, multithreaded parallelism) to reduce latency on complex, many-hop queries over very large graphs. The engine is capable of “warp-speed” graph-wide scanning, billion-scale nodes/edges, and resulting in reductions from multi-day jobs to hours; actual results depend on data volume, hardware, and query complexity.
Rocketgraph includes a GenAI-assisted “Mission Control” interface that lets analysts express intents in natural language; the platform generates executable graph models/queries, visualizes results, and supports iterative, explainable analysis. This approach is positioned to lower the skills barrier without requiring specialist query-language expertise.
The platform emphasizes a property-graph model and “zero-ETL connectors” to get data in quickly, with deployment options in cloud or on-prem. This facilitates “ease of data integration” and “ease of schema creation” as key evaluation criteria. Please inquire about your specific sources and throughput.
By searching for subgraph patterns (many-hop relationships with property constraints) and iterating interactively, analysts can rank risk using network context e.g., proximity to known bad actors or centrality in a ring, rather than treating each event in isolation. Rocketgraph’s graph-wide scanning and real-time, iterative outputs support this workflow. Our depth-first search feature also aids deep path exploration.
FAQ
Graph analytics is designed to reveal relationships and hidden pathways traditional methods overlook. Rocketgraph models entities and connections as a property graph and supports graph-wide, multi-hop pattern search, which is directly applicable to anti-money-laundering and fraud ring discovery.
Rocketgraph’s xGT engine applies high-performance computing (HPC) principles (in-memory, scale-up, multithreaded parallelism) to reduce latency on complex, many-hop queries over very large graphs. The engine is capable of “warp-speed” graph-wide scanning, billion-scale nodes/edges, and resulting in reductions from multi-day jobs to hours; actual results depend on data volume, hardware, and query complexity.
Rocketgraph includes a GenAI-assisted “Mission Control” interface that lets analysts express intents in natural language; the platform generates executable graph models/queries, visualizes results, and supports iterative, explainable analysis. This approach is positioned to lower the skills barrier without requiring specialist query-language expertise.
The platform emphasizes a property-graph model and “zero-ETL connectors” to get data in quickly, with deployment options in cloud or on-prem. This facilitates “ease of data integration” and “ease of schema creation” as key evaluation criteria. Please inquire about your specific sources and throughput.
By searching for subgraph patterns (many-hop relationships with property constraints) and iterating interactively, analysts can rank risk using network context e.g., proximity to known bad actors or centrality in a ring, rather than treating each event in isolation. Rocketgraph’s graph-wide scanning and real-time, iterative outputs support this workflow. Our depth-first search feature also aids deep path exploration.
