Using AI in AML: how to turn potential into practice

Using AI to address today’s AML challenges

To understand where AI brings the most value, it’s worth looking at the core pain points facing AML teams today. The AML landscape keeps changing, with several persistent challenges that AI can help address:

• Detection complexity: Money laundering is increasingly hidden in complex, cross-channel behaviour. AI can combine client insights and uncover patterns that rule-based systems miss.
• Operational burden: Traditional monitoring generates large volumes of false positives. AI helps reduce noise and improve alert quality.
• Talent scarcity: With experienced AML professionals in short supply, AI can streamline repetitive tasks and enable staff to focus on complex investigations.
• Regulatory complexity: Regulations are constantly shifting. AI can help institutions keep pace by optimising compliance and auditability.

So how are financial institutions starting to apply AI in practice to tackle these challenges?

From market trends to real-world application

Fortunately, AI is no longer just a concept: it’s already being put to work across key areas of AML. Here are some of its use cases:

• Alert ranking and augmentation: AI helps prioritise alerts based on risk scoring, improving investigator efficiency.
• Customer risk segmentation: Enabling more accurate and dynamic risk models.
• SAR automation: Natural language models support the drafting of Suspicious Activity Reports.
• Entity resolution: AI links related client records across disparate systems, supporting better client insights.
• AI agents: Early-stage AI assistants can support investigations with contextual data summaries or workflow suggestions.

But even the most sophisticated tools rely on the people behind them. Technology alone isn’t enough.

Why human insight matters

AI cannot operate independently in a high-stakes, regulated domain like AML. Human expertise is essential to interpret insights, validate models, make final decisions and guard compliance.

Strategies for successful AI adoption

With human insight at the core, institutions must also consider how to drive successful adoption across the organization. Moving from experimentation to widespread adoption requires more than just strong model performance. Cultural readiness and cross-functional alignment are just as critical to success.

These factors contribute to AI adoption:

• Value demonstration: Pilot projects underline practical value and help overcome scepticism.
• Education and training: Equip staff with an understanding of what AI can (and can’t) do.
• User trust: If people perceive AI as useful, reliable and intuitive, adoption follows.
• Cross-functional collaboration: Align compliance, risk, IT, and business objectives.
• Leadership support: Senior management plays a vital role in promoting adoption and governance.

Once AI is adopted, the next challenge is building lasting trust among users, stakeholders, and regulators alike.

Fostering trust through transparency

Building trust in AI is about explainability and control.

• Model explainability: Use interpretable models, complemented by documentation and visualisation tools, so stakeholders can verify logic and outcomes.
• Output transparency: Make sure investigators can see not only the AI recommendation, but the data and reasoning behind it.
• Model auditability: Keep full logs of inputs, outputs and model changes — this is essential for compliance.

Transparency creates trust with both users and regulators, and it’s a requirement under emerging AI regulation frameworks. But transparency must go hand-in-hand with accountability. Human oversight remains non-negotiable.

Maintaining accountability

AMLCOs must be authorised to make the final decision, properly reviewing AI-driven outputs before any action is taken. Regulators also expect institutions to document AI-related decisions, demonstrating that outputs are subject to human oversight. Establishing clear accountability frameworks helps align AI-driven compliance strategies with ethical and regulatory standards.

While accountability frameworks provide oversight, long-term success also depends on how AI impacts the people using it.

Supporting employee satisfaction

AI offers not just operational benefits, but also workforce value. Analysts gain time to focus on high-value, judgment-based work instead of repetitive triage, and AI creates new roles in areas like model validation, governance and AI risk management.

Increased automation can enhance employee satisfaction by improving efficiency and clarity in investigative work. By clearly communicating these benefits, organisations can improve buy-in and reduce change fatigue.

As AI takes on a greater role in day-to-day AML work, robust control frameworks are essential to support both employee success and institutional safety.

Creating robust control frameworks

Institutions need to establish AI risk management frameworks to monitor model performance, mitigate bias, and maintain data integrity.

But AI performance also relies on the resilience of the digital infrastructure it operates within, making it essential to address broader technology and security risks that could impact AML effectiveness.

Infrastructure risks

Modern financial institutions need to balance open digital ecosystems with solid security frameworks. Cyber threats like data breaches and system intrusions can cause serious financial and reputational damage. Industry standards offer structured ways to strengthen digital resilience in areas like:

• Threat and vulnerability management
• Supply chain security
• Incident response
• Identity and access management
• Logging and monitoring

AML AI systems are not stand-alone: they must integrate within the wider risk environment of the institution.

AI in the broader risk landscape

AI-based AML systems must integrate into the institution’s Enterprise-Wide Risk Assessment (EWRA) framework.

An EWRA evaluates and mitigates risks that may impact a bank’s profitability, stability, and reputation. AML risk mitigation starts at customer onboarding with KYC checks, sanction screening, and authentication, aligning customers and transactions with the institution’s risk appetite.

Responsible AI use, transparency, and secure digital infrastructure should all be part of AML risk management to reduce exposure from tech-driven tools and third-party systems.

Central to all these elements is the quality and management of data feeding AI models.

Strong data management

AI success depends on one thing above all: high-quality data.

Your institution should have:

• A strong data management vision: a clear strategy for how data is collected, stored and used, in line with business goals and regulatory demands.
• A well-integrated data architecture: allowing data to flow smoothly between systems, integrate well and remain easily accessible.
• Entity resolution: accurately identifying and linking data related to the same entity across different sources.
• Positive label management: training AI models to recognise patterns and predict future occurrences by indicating suspicious instances.
• Proper data classification: allowing AI systems to identify deviations from normal behaviour that may signal suspicious activities.

Use AI data quality controls (like anomaly detection in event-based KYC processes) to improve accuracy and overall data quality. Make sure data is reliable, correct, complete, well-governed and aligned with real-world behaviour and investigative needs.

Along with data quality, managing model risk is critical to meet emerging regulatory requirements.

AI model risk

The European AI Act requires models to be transparent, unbiased, and auditable. Institutions should assess and validate models based on:

• Purpose definition
• Risk classification
• Model inventory and governance
• Ongoing validation and monitoring

With a clear and well-documented AI risk strategy, institutions can use AI effectively while staying compliant, ethical, and resilient. Strong governance ties all these elements together into a coherent framework.

Building a solid governance framework

A strong governance framework sets clear policies for developing, deploying, and monitoring AI models. It also requires cross-functional governance teams to oversee performance and ensure regulatory alignment.

Key elements include:

• Accountability: Clear lines of responsibility and human review of AI outputs.
• Explainability and transparency: Techniques like model documentation and explainability tools to justify decisions.
• Ongoing performance monitoring and model validation: Regular audits, bias detection, and data drift analysis.
• Continuous learning and adaptation: Governance must adapt to new threats, technologies, and regulatory changes.

Strong governance helps financial institutions tackle the complexities of modern AML and remain compliant and trustworthy. Bringing all these elements together requires a strategic, cross-functional effort.

Responsible AI use is a team sport

AI has the power to significantly enhance the effectiveness, accuracy and agility of AML, but it’s no quick win. Success depends on responsible implementation across people, processes, and platforms.

Success depends on:

• Strong human–AI collaboration;
• Transparent and accountable model design;
• High-quality data and governance;
• Organisational alignment and cross-functional adoption;
• A proactive stance on regulatory compliance.

When these elements come together, AI can truly become a trusted ally in anti-money laundering practices.