AI in Financial Crime Compliance: Understanding the Distinction between AI Agents and Agentic AI
If you work in anti-money laundering and countering the financing of terrorism (AML/CFT) compliance, you’re likely familiar with the buzz surrounding the revolutionary potential of artificial intelligence (AI) tools in your field.
Defining AI Agents and Agentic AI
AI agents and agentic AI are two interconnected terms gaining traction in compliance discussions. In a recent speech, Federal Reserve Governor Christopher J. Waller highlighted the ability of agentic AI to enhance risk detection for financial institutions, while a report from McKinsey pointed to financial crime risk management as a key area for agentic AI applications.
Despite the excitement, firms looking to upgrade their compliance tech stacks should be cautious of vendors who may exaggerate their offerings, either by blurring the lines between AI agents and agentic AI or by promoting systems that primarily utilize basic automation.
The Difference Explained
Financial institutions are increasingly recognizing the transformative potential of agentic AI. According to a survey conducted in 2026, 73% of compliance leaders reported they were using or evaluating vendor solutions for customer screening, while 70% were doing the same for transaction monitoring.
However, the term “agentic AI” can refer to both individual AI agents and more complex, coordinated agentic systems. AI agents are autonomous software systems capable of taking actions on behalf of users within a narrow domain. They utilize tools and memory and leverage large language models (LLMs) to automate discrete tasks. For instance, at ComplyAdvantage, these agents are integrated into the platform and informed by a proprietary intelligence graph containing over 23 million enriched entities. They operate without direct human oversight, significantly enhancing speed and efficiency, provided they are orchestrated within governed workflows that ensure an audit trail.
Agentic AI, in its true form, represents an advanced architectural framework that includes:
- Multi-agent collaboration: Multiple specialized agents working together for faster outcomes.
- Dynamic task decomposition: Breaking down complex workflows into manageable tasks.
- Coordinated autonomy: Multiple AI systems working in sync within a human-designed compliance framework.
- Persistent memory: Retaining information long-term beyond single-session interactions.
The Critical Role of Data
Shifting to a genuinely agentic system necessitates consistent access to extensive data, resulting in systems that are more intelligent and autonomous than individual AI agents. This transition is not merely an enhancement of existing software but a fundamental re-engineering of compliance operations, featuring full-stack automation, dynamic risk scoring, and continuous feedback loops for process optimization.
At ComplyAdvantage, the system employs a unified model for all entities and relationships, enabling comprehensive explainability by tracing any agentic decision back to its source.
AI Roadmap for Future Compliance Teams
Each firm’s approach to AI adoption should be tailored to its specific needs, but generally, human-AI collaboration is expected to evolve through three stages:
- Stage 1: Compliance analysts assign simple, repetitive tasks to AI tools.
- Stage 2: Compliance analysts work alongside agents, who perform specific tasks autonomously.
- Stage 3: Full-stack automation enables agentic systems to manage multiple workflows, with human teams setting strategic directions.
Business Advantages of Agentic AI in AML Compliance
The benefits of adopting agentic AI for compliance are significant, as highlighted in the 2026 survey where firms reported increased efficiency, improved customer experiences, and quicker resolution times. These advantages extend beyond compliance, positioning it as a strategic growth driver.
Firms that embrace agentic AI can expect to:
- Reduce regulatory risks: With agents constantly sourcing real-time risk data, compliance processes become more aware and responsive.
- Achieve faster growth: Agentic systems allow for scalable and flexible operations, facilitating smoother customer onboarding.
- Optimize efficiency: Agentic customer due diligence can significantly reduce false positives, improving analyst productivity by 85-90%.
Empowering Compliance Teams with Agentic AI
Agentic AI capabilities offer continuous monitoring and case remediation, addressing the burdens that often overwhelm compliance teams. Key features include:
- Automating low-risk remediation quickly.
- Providing transparency through clear, auditable reasoning and confidence scores.
- Escalating only ambiguous cases for human intervention.
This shift is not about replacing compliance teams, but about empowering them to focus on high-value tasks. Evidence suggests that customers using agentic systems can achieve up to an 85% reduction in costs and efficiently eliminate backlogs.
In conclusion, as the adoption of advanced AI becomes standard in financial crime risk management, firms with the most comprehensive and organized data will be ideally positioned to leverage agentic systems, reaping substantial efficiency gains.
