Financial crime remains a major threat. The United Nations estimates that criminals launder up to $2 trillion annually, yet institutions detect only about 2% of global financial-crime flows. As money-laundering tactics grow more sophisticated, regulators are raising the bar. Manual, rules-based systems are no longer enough as they often generate high false-positive rates and slow down investigations.
Artificial intelligence (AI) and machine learning (ML) are transforming AML programs from reactive to proactive. Platforms like Alessa are using AI to help institutions meet these challenges. Below we explore how AI is improving AML software, the benefits and pitfalls of emerging technologies like agentic AI, and how compliance teams can prepare for the future.
Why AI Is Becoming Essential to AML
Beyond rules: pattern recognition and real-time insight
Traditional AML systems rely on rigid thresholds that often miss subtle risks. AI allows systems to analyze vast datasets, learn what “normal” customer behavior looks like, and identify anomalies that traditional systems overlook.
Key benefits include:
- Real-time monitoring: ML models analyze streaming payments to detect unusual activity within seconds. For example, 62% of institutions use AI and ML for AML, and adoption is expected to reach 90% in 2025.
- Reduced false positives: Predictive models learn from historical investigations, reducing false positives by up to 40%.
- Contextual risk modeling: Behavioral analytics create customer baselines, applying risk scores that adapt to factors like cross-border transaction spikes.
Types of AI in AML
| AI Type | Key AML Applications | Notes |
| Analytical AI | False-positive reduction, peer-group comparisons, dynamic customer risk rating | Uses ML algorithms to analyze large datasets more efficiently than humans. |
| Generative AI | Summarizing documents, extracting data, drafting suspicious-activity reports | Produces human-readable content from structured and unstructured data. |
| Agentic AI | Autonomous monitoring, sanctions investigations, KYC refreshes | Digital agents handle workflows with minimal human oversight. |
AI-Driven Transaction Monitoring
Transaction monitoring is the backbone of AML compliance. Criminals increasingly exploit real-time payment rails and digital assets. AI enables compliance teams to keep pace.
Alessa’s transaction monitoring software provides real-time, periodic, or event-based monitoring across a wide array of products and services such ACH, loans, credit cards, and investments. Benefits include:
- Real-time screening of counterparties against sanctions lists.
- Configurable workflows that allow investigators to collaborate and document actions.
- Fewer false positives and quicker case resolutions.
Industry research confirms that AI-enabled monitoring reduces noise and increases accuracy, but success depends on improving data quality and updating rules regularly.
AI-Powered Identity Verification and KYC
Know-Your-Customer (KYC) processes are critical for preventing illicit activity at onboarding. Alessa’s identity verification solution uses AI to:
- Verify identities in real time.
- Screen against sanctions and politically exposed persons (PEPs).
- Provide continuous monitoring for changes in customer status.
Generative AI supports KYC by summarizing identity documents and pre-filling due-diligence reports, saving analysts significant time.
Agentic AI and Digital Workers
Generative AI assists investigators by producing summaries and draft reports. Agentic AI goes further, allowing digital agents to autonomously perform transaction monitoring, KYC refreshes, and sanctions investigations.
According to McKinsey, productivity gains from agentic AI range from 200% to 2,000%. Nasdaq Verafin’s agentic AI workforce launched in July 2025 reduced sanction-screening alerts by more than 80%, freeing human investigators to focus on high-value cases.
Responsible AI: Governance, Bias, and Transparency
AI adoption must be paired with strong governance. Risks include bias, lack of transparency, and over-reliance on automation.
Best practices include:
- Establishing governance frameworks and audit schedules.
- Centralizing and cleaning customer data.
- Training compliance teams on explainability tools.
- Continuously monitoring performance and refining models.
Alessa’s blog on reducing false positives emphasizes data quality and frequent rule updates as foundations for responsible AI.
Future Trends: Beyond 2025
Key developments shaping the next few years:
- Growth in RegTech solutions projected to exceed $22 billion.
- Increasing integration of blockchain in KYC and transaction monitoring.
- Transparency requirements for beneficial ownership.
- Digitization of KYC onboarding with biometrics.
- Adoption of privacy-enhancing technologies such as zero-knowledge proofs.
- Stronger cross-border regulation for crypto exchanges.
Alessa’s 2025 AML compliance trends report highlights that 75% of professionals now prioritize efficiency and that AI adoption is accelerating across the industry.
Preparing Your Organization for AI-Powered AML
Practical steps for adoption:
- Assess current capabilities and identify where AI delivers the most impact.
- Choose the right technology mix of analytical, generative, and agentic AI.
- Prioritize data governance to ensure quality inputs.
- Create governance protocols for oversight and explainability.
- Engage and train stakeholders to build confidence and adoption.
- Continuously monitor and refine AI models.
Next Steps
AI is reshaping AML compliance in 2025. From reducing false positives in real-time monitoring to deploying digital agents that handle resource-intensive tasks, AI enables faster and more accurate detection of financial crime.
Platforms like Alessa show how AI can be integrated responsibly, balancing automation with transparency. By adopting AI with strong governance and high-quality data, institutions can protect themselves and their customers while staying ahead of evolving regulatory expectations.
Alessa