Agentic AI is rapidly reshaping the way financial institutions strengthen their anti-money laundering (AML) capabilities.
As growing transaction volumes, evolving criminal techniques, and tightening regulations place increasing pressure on compliance teams, many firms are looking for new ways to improve accuracy without overwhelming investigators, claims Hawk.
According to Hawk’s chief data scientist, Felix Berkhahn, the answer lies in deploying agentic AI to optimise detection systems, reduce false positives, and uncover previously unseen risks.
A major challenge for financial institutions is how they set and adjust transaction monitoring thresholds. Setting thresholds too low floods analysts with false positives and strains limited resources, while thresholds that are too high allow suspicious behaviour to slip past entirely. Traditional threshold tuning relies heavily on manual processes where data scientists periodically review historical data, propose a single threshold adjustment, and validate it through time-consuming “pseudo-investigations”. These static models assume that criminals repeat behaviours, even though they constantly shift tactics. Agentic AI removes this bottleneck by testing numerous threshold combinations simultaneously, using real-time data to simulate the impact on alert queues and detection coverage. What once took weeks can now be completed in minutes.
Threshold tuning, however, is only part of the challenge. Financial crime evolves faster than traditional rule-based systems can respond, and designing new rules requires deep typology expertise. When new laundering techniques emerge, analysts must translate complex patterns into workable logical conditions, often blending behavioural anomalies, network analysis, and timing patterns. Agentic AI operates as an on-demand data scientist, analysing alerts, SARs, and investigation outcomes to pinpoint where rule logic falls short. If an agent identifies a recurring pattern — for instance, repeated transfers between $8,000–$9,500 to high-risk merchant categories — it can recommend new rules to close that gap before criminals exploit it further.
A broader concern for compliance leaders is understanding what their systems cannot detect. Scenario coverage mapping exposes blind spots by analysing typologies, real-world activity, and emerging criminal trends. Many existing systems fail to recognise sophisticated laundering patterns where criminals combine structuring, layering, and trade-based methods in ways that are individually benign but collectively suspicious. Agentic AI highlights these gaps, explains why they matter, and provides documentation so investigators can determine which issues represent genuine risk. While large-scale scenario analysis can be expensive, combining traditional AI with agentic AI provides a cost-effective model: the first scans vast transaction volumes, and the second performs deeper investigative work on flagged cases.
At the centre of this approach is the Hawk AML Analyst Agent, built specifically for compliance and financial crime teams. Unlike generic AI tools, it embeds domain knowledge directly into its reasoning, using specialised components such as a Typology Agent to extract actionable signals from typology documents and a Data Enrichment Agent to interpret complex case data. By mirroring human investigative workflows, the Analyst Agent enhances existing detection systems, provides clear and explainable reasoning, and continuously adapts to new regulatory expectations. For compliance leaders seeking stronger defences against increasingly complex criminal activity, agentic AI offers a scalable, intelligent, and auditable partner.
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