How Agentic AI Can Support Compliance Teams With Anti-Money Laundering Due Diligence

Over the last year, agentic AI has dominated headlines. From big tech partnerships, like AWS and OpenAI partnering on advanced AI workloads, to agentic AI tools being broadly integrated across industries like retail, government, and financial services, AI agents are being integrated into everyday lives and workflows. According to McKinsey, 62% of organizations are already experimenting with AI agents, and 64% say that AI is enabling their innovation, demonstrating AI’s rapid path to adoption in the enterprise.

Agentic AI is also on the path to redefining the role of human workers. A PwC survey found that 66% of companies that have adopted AI agents have increased productivity. Since many AI agents will be able to conduct tasks without human intervention, the human workers will be able to focus on more strategic tasks, leaving the tedious administrative work to their digital colleagues.

One compelling and critical use case for agentic AI within financial services is in financial crime prevention. Money laundering cases reported to the U.S. Sentencing Commission increased by 45% between 2020 and 2024, underscoring a quickly rising challenge, causing compliance headaches nationwide.

When it comes to compliance processes, agentic AI can have an impact on customer due diligence (CDD) by integrating agents into anti-money laundering (AML) workflows, which can support alert resolution and case handling to reduce false positives for low-risk entities.

For financial institutions to have impactful outcomes from the use of AI agents, they need to adopt AI responsibly and deliberately. Below are five key considerations for compliance leaders:

1. Letting AI agents handle the manual tasks

Compliance officers are often worn thin on resources when it comes to team size, budgets, and time constraints, with over half reporting they are burned out at work and nearly half experiencing anxiety. Especially in the functions of CDD and the know your customer (KYC) processes, doing alert reviews to identify and clear false positives can be a big strain on compliance teams, which can open the door to risks and delays.

When agentic AI is implemented to support these strenuous processes, it can automate some of those time-consuming tasks, like monitoring for risks continuously and updating customer profiles as soon as there is a change in information. AI agents can review and triage alerts by removing false positives at a higher rate than manual reviews, which also lets higher-risk cases go straight to human analysts so their time can be used efficiently. Agents can also conduct initial customer screening checks against essential risk data, politically exposed persons (PEPs), adverse media, and sanctions, then generate alerts for any matches.

2. Data transparency

As with all agentic AI, effectiveness and trust begin with the data on which systems are trained and governed. Beyond strong data-cleaning practices, clear data lineage, and comprehensive record-keeping to minimize hallucinations or bias, firms must ensure regulatory defensibility through robust model governance. This includes using systems overseen by a formal Model Review Board (MRB) that manages the full model lifecycle, conducts regular testing, and relies on “golden datasets” to prevent model drift over time. Granular, explainable AI is especially critical in this context. For example, our LLM-driven classification pipeline categorizes adverse media across 34 distinct risk subcategories, enabling precise, auditable decision-making. This level of transparency and control not only satisfies increasing regulatory and auditor scrutiny, but also reinforces confidence in how AI supports AML and CDD outcomes.

3. Assess where agentic AI will be most effective

AI adoption doesn’t mean an organization needs to replace its existing tech stack. When assessing how agentic AI can be used within CDD, compliance officers should establish a proof of concept, test out how agentic systems can be used, and build out use cases as the adoption maturity increases. This can help assess if the most effective use for AI adoption is as little as using it for initial screenings or as big as using it for full alert remediation.

4. Utilizing AI to enhance compliance expertise

While automation handles routine triage, the true value of agentic AI lies in its ability to elevate the compliance professional’s role from administrative to strategic. This shift is not about the displacement of teams, but about refocusing human intuition on the highest-value work – such as complex investigations where moral judgment and nuanced interpretation of criminal intent are required.

Expertise is further enhanced when AI functions as a “digital colleague” within the workflow. Current design trends favour anthropomorphised agents because they foster psychological safety; by providing clear, natural-language reasoning for every suggestion, these systems help analysts learn from the AI’s logic rather than just accepting a binary result. As organizations scale, this allows the compliance function to become a proactive driver of growth, with analysts taking on sophisticated new responsibilities in model risk management, AI testing, and strategic forensic investigation.

5. A strong foundation

A resilient, cloud-native platform is the prerequisite for speed. You cannot bolt AI onto a broken architecture and expect it to work well; the most successful deployments arise from a unified data lifecycle, from ingestion to final case resolution. Maintaining a single source of truth for risk data ensures that models remain consistent across different geographic regions. In this context, agentic tools perform best when integrated into an ecosystem with pre-existing, strong frameworks for testing, data protection, and oversight.

Redefining AML compliance in the age of agentic AI

Compliance leaders are at an inflection point – as agentic AI tools become more advanced and financial crime continues to increase, they need to ensure they have the right AML and CDD protections while assessing what AI tools can support their goals. Agentic AI empowers financial institutions to scale KYC efforts while freeing teams to focus on complex, high-value work. Paired with human expertise, AI drives faster alert triage and case resolution, strengthening risk protection and reducing costs, which is truly reshaping the future of AML due diligence.