Financial Crime
Today's criminal networks evolve faster than legacy AML systems can adapt. Static rules and siloed monitoring leave institutions overwhelmed by false positives, while genuinely harmful activity continues to move undetected across products, entities, and jurisdictions. The problem is not a lack of data, it is a lack of connected understanding.
DeepFlow addresses this by introducing a cross-silo financial crime brain. By combining agentic AI with graph-based reasoning, DeepFlow builds a living model of how transactions, customers, accounts, behaviours, and decisions relate to one another across the organisation and beyond it. Rather than assessing alerts in isolation, DeepFlow understands context: why behaviour is suspicious, how it connects to other activity, and where risk is genuinely concentrated. This allows emerging typologies and hidden relationships to surface naturally, without waiting for new rules to be written or thresholds returned.
Crucially, this intelligence is delivered with governance at its core. AI agents coordinate detection, prioritisation, investigation, and reporting across silos at machine speed, while humans remain in control of the end-to-end process. Instead of managing thousands of disconnected alerts, compliance teams are presented with clear outcomes, investigative narratives, and decision points exactly where judgement and regulatory accountability are required.
Every decision path is transparent, explainable, and fully auditable, giving confidence not just in conclusions, but in how those conclusions were reached.
The result is a step-change in effectiveness: dramatically fewer false positives, faster and more consistent case resolution, and compliance teams redeployed from reactive alert clearing to strategic oversight.
With DeepFlow, financial crime compliance evolves from a fragmented, defensive cost centre into an intelligence-led capability, one that strengthens regulatory confidence, protects customers, and safeguards the institution as a whole.
DeepFlow addresses this by introducing a cross-silo financial crime brain. By combining agentic AI with graph-based reasoning, DeepFlow builds a living model of how transactions, customers, accounts, behaviours, and decisions relate to one another across the organisation and beyond it. Rather than assessing alerts in isolation, DeepFlow understands context: why behaviour is suspicious, how it connects to other activity, and where risk is genuinely concentrated. This allows emerging typologies and hidden relationships to surface naturally, without waiting for new rules to be written or thresholds returned.
Crucially, this intelligence is delivered with governance at its core. AI agents coordinate detection, prioritisation, investigation, and reporting across silos at machine speed, while humans remain in control of the end-to-end process. Instead of managing thousands of disconnected alerts, compliance teams are presented with clear outcomes, investigative narratives, and decision points exactly where judgement and regulatory accountability are required.
Every decision path is transparent, explainable, and fully auditable, giving confidence not just in conclusions, but in how those conclusions were reached.
The result is a step-change in effectiveness: dramatically fewer false positives, faster and more consistent case resolution, and compliance teams redeployed from reactive alert clearing to strategic oversight.
With DeepFlow, financial crime compliance evolves from a fragmented, defensive cost centre into an intelligence-led capability, one that strengthens regulatory confidence, protects customers, and safeguards the institution as a whole.