Agentic AI Reshapes AML Investigations
AML analysts enter the profession to help fight financial crime, yet many find themselves overwhelmed by inefficient processes and constant false alerts. This not only drives high turnover rates within the first year but also undermines the quality of investigations.
When staff are burdened by unnecessary alerts, they risk missing the genuine criminal activity hidden among them. Agentic AI promises a more effective approach, significantly shifting the landscape of AML investigations.
The Evolution of AI in Financial Crime Detection
Traditional AI has long been applied to financial crime, primarily to detect suspicious activity through structured data. Generative AI (GenAI) extended these capabilities by producing content such as draft reports. However, agentic AI goes a step further, offering an orchestrated network of AI-driven agents, each focused on a specific task. These include:
- Data-gathering agents that consolidate information.
- Typology agents that classify risks.
- Narrative agents that draft suspicious activity reports (SARs).
By reducing false positives and triaging alerts more intelligently, agentic AI enables analysts to dedicate more time to genuine threats.
Enhancing Case Assessments
The technology enhances case assessments by accessing multiple data sources in real time, delivering a complete picture of risk. This helps teams filter out irrelevant alerts quickly while escalating higher-priority cases. It also recommends queue prioritisation, drawing from historical resolution patterns to ensure high-risk cases receive prompt attention. While automation plays a major role, agentic AI still supports a human-in-the-loop approach, ensuring compliance teams remain in control of complex, high-stakes decisions.
Streamlining the Investigative Process
For investigators, the most significant advantage lies in the streamlined process. Analysts must log into multiple systems, collect scattered data, and manually document findings. Agentic AI accelerates this by gathering data from both internal and external sources, including transaction records, customer databases, and AML registries, to produce a structured overview with key red flags already identified. This automation cuts the time spent on repetitive work and increases accuracy.
The system then labels cases by typology, instantly identifying whether the activity suggests money laundering, fraud, or another form of financial crime. Analysts receive cases that are already categorised, enabling them to focus on investigation and decision-making rather than pattern recognition. Agents also recommend next steps, ensuring consistent workflows and reducing the possibility of oversight.
Standardising Documentation
Another key feature is the ability to capture rationale consistently across cases. Instead of analysts spending time writing varying explanations that may lack regulatory clarity, the AI standardises documentation, making it audit-ready. This reduces compliance risks and ensures investigations are transparent to auditors and regulators alike.
Accelerating SAR Creation
SAR creation, often a time-consuming final step, is also accelerated. Rather than beginning with a blank page, analysts receive a draft that aligns with regulatory standards, allowing them to refine instead of create from scratch. This improves the quality and turnaround time of reports.
Customisable Workflows
The AML Analyst Agent is designed to take full advantage of agentic AI while remaining adaptable. Financial institutions can customise workflows by uploading their policies and procedures, with no coding required. This flexibility allows institutions to determine where AI can act independently and where human approval is necessary.
Importantly, Hawk’s system functions as an overlay, operating on top of existing AML infrastructure. It integrates via APIs or directly through user interfaces, meaning institutions don’t need to replace existing systems. Designed with auditability in mind, the solution provides detailed logs of every action and a visual map of decision-making, complete with citations and confidence scores. This ensures transparency and regulatory compliance.
The Future of AML Investigations
As financial crime becomes increasingly complex, the need for solutions that enhance efficiency while safeguarding compliance grows. Agentic AI offers a promising path forward, reducing analyst fatigue and ensuring investigations are both faster and more reliable.
