A guide to the transformative role of agentic AI in AML

As compliance with anti-money laundering (AML) regulations has become both more complex and more important for financial institutions (FIs), advanced technology has taken on a central role in their operations. 

Agentic AI represents the latest frontier in technology-driven AML compliance. This article explores how agentic AI works, its key compliance use cases, and how it can help your compliance team meet regulatory requirements while driving growth. 

What is agentic AI?

Agentic AI refers to AI systems (or ‘AI agents’) that can act autonomously with no or minimal human intervention, independently pursue specific goals, and make context-aware decisions. Key features of agentic AI are: 

• Autonomy: AI agents make decisions based on existing programming, learned knowledge, and environmental inputs. 
• Goal-oriented behavior: Agents work towards defined outcomes and can adapt their behavior to achieve these more effectively. 
• Learning capability: Agentic AI systems use ML to optimize their performance over time. 
• Multi-agent interaction: Agentic AI tools can be configured to allow communication between different agents and non-AI tools to carry out complex workflows. 

How does agentic AI differ from generative AI?

Generative AI (GenAI) refers to AI systems that create content (such as text, images, or code) based on patterns learned from large datasets. Agentic AI, on the other hand, makes decisions and executes tasks. Unlike genAI, agentic AI can make decisions and carry out independent reasoning without the aid of pre-defined instructions. 

Key applications of agentic AI in AML: 4 examples

These features have clear value for compliance teams, allowing them to automate tasks and even entire workflows to significantly enhance operational efficiency. In particular, agentic AI can be used for these AML processes: 

1. Autonomous transaction monitoring and pattern recognition

Transaction monitoring is crucial to AML compliance, allowing you to detect and investigate payment patterns that may be linked to criminal activity. Agentic AI can analyze transactions against risk thresholds and expected financial behavior in real-time, generating alerts for any suspicious transactions without delay. 

Whereas older, rule-based systems can generate large numbers of false positives, transaction monitoring agents can learn from feedback to adjust overly sensitive rules or develop new ones to detect emerging crime methodologies. 

Legacy transaction monitoring systems, especially those that rely on manual checks conducted by analysts, drain compliance resources and increase your exposure to risk. By automating the process, agentic AI improves both accuracy and efficiency in transaction monitoring. 

2. Enhancing KYC and due diligence

The efficiency of your know your customer (KYC) and customer due diligence (CDD) procedures is critical – you must ensure every customer you take on presents an acceptable level of risk to your business without increasing customer churn through a drawn-out or repetitive onboarding experience. 

AI agents can streamline KYC and CDD by collecting identifying information supplied by customers and verifying this with appropriate documentation. They can then use this to screen customers against databases such as sanctions lists, adverse media, and politically exposed person (PEP) data, using any matches to identify the risks a customer presents. 

If customers cross a certain risk threshold, defined as part of your business-wide risk assessment, AI agents can independently carry out enhanced due diligence (EDD). This means lower-risk customers can be swiftly onboarded without wasting compliance resources. In the highest-risk cases, agents can generate alerts for compliance analysts to review, ensuring their time is reserved for the most important decisions.  

After customer onboarding, AI agents can continuously monitor risk data for new customer matches, empowering you to make data-driven decisions on customer accounts. 

3. AI-driven risk scoring and dynamic thresholding

Static, rule-based risk scoring can significantly restrict your operational efficiency, forcing your analysts to spend large amounts of time reviewing legitimate customers or transactions, thanks to alerts generated by inflexible rules.  

Agentic systems, however, can take various contextual factors into account to create dynamic thresholds for risk scoring across customer or payment screening and transaction monitoring, such as a customer’s location, occupation, transaction history, and previous alerts. Agentic AI can identify false positives before passing on cases for analysts to review, ensuring they are free to focus only on genuine threats. 

4. Automated suspicious activity detection and SAR generation

In cases where suspicious activity has been identified according to these dynamic risk thresholds, agentic AI can pre-fill suspicious activity reports (SARS) with the necessary information. 

Since regulators typically require SARs to be completed according to standardized formats, agents can ensure consistency while removing the need for analysts to manually summarize multiple alerts and copy data from one system into another. 

Business brief: Agentic AI and AML compliance

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Key benefits of agentic AI in AML compliance

In our 2025 State of Financial Crime survey, global compliance leaders told us they struggle with factors such as a lack of real-time visibility into their risks (45 percent of respondents), inflexible transaction monitoring rules (41 percent), and a lack of effective case management (35 percent). 

What are the main limitations to your organization’s current approach to financial crime detection?

Agentic AI can address many of the compliance challenges you face, with benefits including:  

• Reduced false positives: Agentic AI can reduce the rate of false positives by increasing the accuracy of searches and implementing dynamic risk thresholds. 
• Enhanced operational efficiency: By automating simple tasks and complex workflows, AI agents can significantly reduce manual workloads and let your compliance analysts devote themselves to higher-risk activities. AI agents’ data analysis and alert handling capabilities allow investigations to be completed much faster. 
• Streamlined reporting: The increased speed of compliance checks and pre-filled SARs allows you to fulfill your reporting requirements and proactively demonstrate compliance to regulators. 
• Scalability: By automating key AML processes, AI agents allow you to handle ever-increasing customer and transaction volumes without proportionally increasing your compliance spend. While they can never replace human analysts, AI agents will allow you to increase your customer acquisition rate without hiring a spiralling number of analysts to handle them. 
• Continual optimization: AI agents learn from previous experiences, adapt to new circumstances, and act according to your goals and KPIs, allowing your compliance function to constantly improve its performance over time. 

Overcoming challenges in agentic AI adoption

As with any emerging technology, agentic AI requires a degree of preparation to be implemented effectively. In particular, you should consider these factors when adopting agentic AI systems. 

1. Regulatory compliance and explainability in AI-driven AML

Global AI regulation remains in its early stages, with no unified international approach yet in place. However, explainability has emerged as a key theme in regulatory demands around AI use, with financial authorities recognizing the transformative potential of AI in compliance while making clear that systems must be transparent. 

Despite this, in our annual survey of compliance decision-makers, 91 percent of respondents claimed to be comfortable compromising explainability for greater automation. This trade-off is not only risky, but needless. Regulators will expect you to demonstrate the reasoning behind automated compliance actions, which makes the use of explainable AI (XAI) a priority. To ensure you are able to do this, use AI agents that automatically create detailed and accessible audit trails as part of investigations. 

When deploying AI-based compliance solutions, how comfortable are you with compromising explainability in exchange for greater automation and efficiency?

2. Addressing bias, data privacy, and model transparency

Rather than treating agentic AI as an instant compliance fix, you should recognize the importance of the data it operates on. If the information used by AI models is biased or unrepresentative, this may be reflected in outcomes, creating issues around accuracy and impartiality. In January 2024, the New York Department of Financial Services (NYDFS) raised concerns about algorithmic bias in financial technologies, emphasizing the need for fairness and precision in automated decision-making processes.

In addition to AML regulatory requirements, you must also conform to regulations around data privacy. GDPR regulations in Europe, for example, impose strict data protection obligations. However, ensuring explainability and model transparency without compromising customer data can be a difficult balancing act. 

You should look for vendors able to demonstrate compliance with regulations like GDPR and respected international standards such as ISO 27001. You should also compile a list of features to look for when assessing vendor capabilities around data protection, such as: 

• Cloud-native software. 
• Encryption capabilities. 
• Dedicated information security training and support. 
• Identity authentication.
• Configurable passwords and role-based permissions.

3. Integration with legacy AML systems and operational workflows

Agentic AI’s transformation of AML lies in its ability to help firms achieve compliance and business objectives at the same time. The most effective agentic compliance tools, therefore, come with integration capabilities that guarantee a minimum of downtime and don’t need you to overhaul your entire tech stack. 

Solutions with modular architecture and human-in-the-loop design allow you to streamline implementation and enhance existing workflows. The quality of your underlying data also remains crucial when integrating AI agents. Poor-quality (inaccurate, outdated, incomplete, or duplicated) data will create false positives and misleading explanations for decisions, undermining the benefits of agentic AI. 

One solution is to look for a RegTech vendor that can combine agentic AI capabilities with best-in-class data while investing in data governance to ensure your systems function correctly. 

The future of agentic AI in financial crime prevention

AI already has many use cases across financial crime prevention, from analyzing customer data during screening processes to detecting suspicious transactions. The effect of agentic AI will be to carry out this work faster and more efficiently, allowing firms to scale their operations while improving their regulatory compliance.

“At the cutting edge is agentic AI. These are systems that are acting with autonomy to decision-control outputs, which is something that if you were to think back one or two years ago was seen as ‘maybe we’ll never quite get there’, and here we are, with agentic AI starting to be implemented at firms that are really looking to push the cutting edge.”

Guy Huber, Principal, FS Vector, in our webinar on AI regulation and the future of AML

As financial crime becomes more complex in scope and more challenging to detect, global AML regulations are likely to increase operational demands on firms to keep up. Your ability to strategically introduce AI agents into your compliance function will allow you to meet these and grow your revenue without a similar increase in your compliance spend. On a wider scale, the improvements in data insights and operational efficiency caused by AI agents will lead to an improved ability to detect crime, particularly in cross-border cases where inconsistent data and stretched resources currently hold back investigations. 

However, while agentic AI is a powerful tool for improving financial crime compliance, it should augment human compliance teams rather than replace them. This is the case for both regulatory and business reasons. AML regulations give authorities the power to hold compliance officers responsible for compliance failures, making human-in-the-loop systems essential for firms to remain auditable. Meanwhile, agentic AI is most effective when used to complete lower-risk tasks while leaving high-level investigative and policy decision-making to human experts. The future of financial crime prevention involves a hybrid model of human and AI agents working together. 

Moving toward an AI-powered AML framework

As technology continues to transform financial services, embracing automation and agentic AI as part of your compliance setup can give you a significant competitive advantage. ComplyAdvantage partners with thousands of forward-thinking FIs to provide them with AI-powered AML solutions designed to help them detect financial crime risks, fulfill regulatory requirements, and turn compliance into a driver of growth. 

ComplyAdvantage’s innovative AI capabilities include: 

• Real-time updates to risk data: Our proprietary risk intelligence empowers firms with data sourced straight from regulators and refreshed by automated systems. Compliance teams use this information for effective sanctions, adverse media, and politically exposed person (PEP) screening. 
• Entity resolution: Our AI systems analyze data to assess how entities are identified and linked to key data points. We use this to create consolidated views of customer risks on a single profile. 
• Enhanced risk detection in transaction monitoring: Our transaction monitoring solution detects hidden patterns of suspicious customer behavior, allowing you to gain insights into new crime typologies and investigate alerts without delay. Where existing rulesets cannot detect specific behaviors, our AI capability fills the gap to precision. 
• Continuous performance optimization: With comprehensive data dashboards and real-time performance insights, you can understand your compliance team’s performance and make necessary changes to workflows.