Why AML Investigators Are Drowning Despite Good Technology
The bottleneck isn't data or even tooling — it's analyst time. Building and querying transaction-network graphs, running PageRank to surface influential nodes, applying community-detection algorithms to identify structuring rings: each of these is meaningful work, but doing it case-by-case for every suspicious activity cluster is unsustainable at scale. Meanwhile, the typology patterns your team has accumulated in Slack threads and schema design sessions represent hard-won knowledge that's largely locked in unstructured text.
An Agent That Turns Graph Knowledge into Continuous Detection
This agent is built from your existing investigator knowledge — specifically the typology pattern reviews and Neo4j schema design discussions your team has already had. From that foundation, it constructs transaction-network graphs automatically, runs PageRank and community-detection algorithms on incoming data, and surfaces high-risk entity clusters with supporting evidence for your compliance data science head's SAR review. Nothing gets filed without human sign-off; the agent's job is to make each SAR decision faster and better-supported. In practice, teams operating setups like this see SAR analyst throughput improve roughly 3x and network-based detection rates rise around 35% — the agent goes live and producing results in approximately eight weeks.
The Revenue Case for Better Detection
For a compliance fintech, detection capability is a product differentiator. Better network-based detection means fewer false negatives — which reduces regulatory exposure and the reputational cost of missed typologies. On the growth side, an agent that triples analyst throughput means your compliance team can support a larger customer base and higher transaction volumes without proportional headcount growth. That's the capacity that makes enterprise contract wins operationally viable.
Does this require us to have Neo4j already deployed, or can it work with Amazon Neptune?
The agent can be configured for either Neo4j or Amazon Neptune depending on your existing infrastructure. The graph construction and algorithm logic is abstracted above the specific database layer.
How does the agent learn our firm's specific AML typologies?
It mines your investigators' existing Slack threads on typology pattern reviews and schema design sessions — your institutional knowledge about what laundering looks like in your data becomes the foundation for the detection logic.
What does the compliance data science head actually review, and how much work is it?
The agent surfaces pre-built entity clusters with supporting graph evidence, reducing the review task from open-ended investigation to judgment on a prepared case. The goal is to make each sign-off decision faster without removing human accountability from the SAR filing process.