If last year was shaped by the rapid growth in the use of generative AI applications, like GPT or Gemini, then this year’s breakthrough technology is already locked in – agentic AI. From financial services through to the heart of the government, AI agents occupy the innovation roadmap – and rightly so.
Agentic systems carry out tasks autonomously without direct human supervision. This means greater automation and business efficiencies, and alleviating human resources from manual or administrative work to focus on more strategic – and often more compelling – tasks.
In financial crime prevention, agentic AI is already having an impact on compliance processes, particularly within customer due diligence (CDD). Integrating agents into anti-money laundering (AML) workflows can help with swift case handling and alert resolution for low-risk entities, reducing false positives.
In fact, Greenlite, an agentic platform embedded within our software, can reduce analyst workloads by up to 95%, and in turn, enhance the speed and accuracy of threat detection and prevention.
But to truly extract value from AI agents, financial institutions must first set themselves up for success with the right implementation and deployment. Here are my five learnings on how AI agents can strengthen risk detection and enhance efficiency simultaneously.
- Agentic AI enables you to do more with less
Compliance officers are used to constant pressure on their time, budgets, and teams, which invariably means most compliance functions cannot work at their full potential. During the know your customer (KYC) and CDD processes, alert reviews to identify and clear false positives are often a particular drain on compliance resources.
This means genuine risks can get lost amid backlogs of unnecessary alerts, while a lack of capacity can delay ongoing checks on higher-risk customers.
Agentic AI systems can automate various manual, low-risk CDD tasks that suffer from slow, manual workflows. They conduct initial customer screening checks against essential risk data, including sanctions, politically exposed persons (PEPs), and adverse media, and generate alerts for any matches.
AI agents can also review and triage alerts, removing false positives with a higher efficacy rate than manual reviews. This also lets higher-risk cases go straight to human analysts, who can preserve their time and energy for the cases that actually matter.
Finally, AI agents monitor for risks continuously, updating customer profiles as soon as they detect changes in their information and allowing firms to move from periodic reviews towards perpetual KYC (pKYC).
- Tailor your AI adoption to your risk appetite
While agentic AI can work across the spectrum of CDD operations, you don’t need to take an ‘all or nothing’ approach to its adoption. As with any element of AML compliance, your existing tech stack, business-wide risk assessment, and risk appetite will factor into your AI implementation. In any case, you should:
- Establish a proof of concept.
- Test the possibilities of agentic systems.
- Build a range of use cases as your maturity increases.
Depending on your risk appetite, this might involve only using agentic AI for initial screening checks and setting a lower threshold for human reviews, while including the option of using it for full alert remediation in your tech roadmap.
- Agentic AI is about scaling your business, not replacing your team
Fundamentally, the role of agentic AI is to automate tasks, not eliminate human teams. Trying to find out customer information by carrying out manual searches is a waste of time for compliance analysts when this work could be done by AI agents, leaving them free to concentrate on more complex, higher-risk investigations.
In the longer term, this provides a significant boost to firms trying to scale, allowing them to take on more customers without having to expand their compliance team just to keep up with data collection requirements when onboarding customers.
With agentic AI in place, compliance teams have the potential to become more proactive as their companies grow, taking on a more strategic role across businesses as compliance becomes a driver of growth. Meanwhile, as AI expertise becomes essential, not optional, for compliance leaders, newer roles around agentic AI testing and management will become increasingly important.
- Always prioritise explainability
While regulators recognise AI’s potential to enhance compliance, explainability has become a key consideration in how they assess firms’ use of AI tools, and failing to demonstrate transparency carries the threat of enforcement action.
The success of your agentic AI systems depends partly on the data you use. Understanding the lineage of your data, having data-cleaning practices in place, and keeping records of investigations all ensure transparency while minimising the possibility of AI bias or hallucinations. Model risk management practices and regular testing can also help to balance efficiency with explainability.
One way to achieve this is to combine financial crime and AI expertise, ideally by partnering with a vendor whose solutions are specific to AML rather than an AI generalist. These vendors can provide a rigorously tested solution, comprehensive documentation, and tailored support for full auditability.
- People and policies remain paramount
As transformative as agentic AI systems are, technology tools remain just one part of your compliance operation. Having an effective team of analysts and a written compliance programme is critical to efficient AML operations. Agentic AI is most effective when putting an already-robust compliance programme into practice, while its resource-saving benefits only matter if your team can redeploy these resources elsewhere.
These factors will also ensure smooth integration into your existing setup. It can be all too easy to assume that adopting AI tools means creating a governance framework around their use from scratch, but this tends not to be the case in well-designed compliance programmes. Ensuring you have sound, detailed structures around issues like testing, oversight, and data protection will allow you to add agentic AI-specific policies without overhauling existing rules.
As financial crime becomes increasingly sophisticated, often powered by its own AI models, it’s imperative that financial institutions stay one step ahead of bad actors. This starts with robust customer due diligence and the right AML protections, underpinned by the very latest AI breakthroughs.
Agentic AI enables businesses to scale their compliance function to meet KYC demand, while alleviating team time to focus on more strategic or pressing cases. When paired with human expertise, it’s highly effective and drives real business value in swift alert triaging, reviews, and closures, providing risk protection while reducing costs. It’s a win-win outcome that shapes the future of AML due diligence.
