AML in the Age of AI & Cyber Risk: Why Your Teams Need to Be Prepared

Every week it seems there’s a new twist in how cybercrime and AI are being weaponised to launder money. If you're thinking traditional AML training is still enough, think again.

The Risk Landscape Is Shifting Fast

AI is no longer just a tool for business and compliance. It’s also what criminals are using.

Deepfakes and synthetic identities are being used to onboard fake customers or impersonate executives, often fooling even experienced teams. Financial regulators like FINRA warn this synthetic fraud could cost the industry tens of billions by 2027.

Cybercriminals are amplifying these risks using AI-powered phishing, malware, and vishing attacks, some described as the new “nuclear bomb” of scams due to their scale and rapid impact.

Meanwhile, regulators are responding: the NY Department of Financial Services has issued guidance urging firms to embed AI-related cyber risk into governance and training.

AI’s Dual Role: Boosting Efficiency and Creating Risk

AI can be a huge asset but it must be wielded carefully.

On the upside:

• It identifies patterns and anomalies in huge data sets that manual teams simply can’t handle in real time 

• It can cut false positives, letting investigators focus on high-risk alerts, running AML investigations more effectively and efficiently 

On the downside:

• Attackers use adversarial AI to trick models or poison datasets; classic AI models without robust safeguards pose vulnerabilities 

• Increased AI use exposes data security gaps, many firms still lack oversight of internal AI tools or formal policies governing them 

  
  

Compliance Priorities Have Shifted

According to the 2025 Investment Management Compliance Testing Survey, AI and predictive analytics are now the top concern for compliance officers, followed by AML readiness and cybersecurity controls.

Training Needs to Catch Up

Here’s why conventional AML training is no longer enough:

• It's built around legacy typologies; manual record review, rule-based detection, etc. not AI-driven deception or insider threats using generative tools.

• It lacks real-time digital case studies (e.g., GenAI scams, synthetic fraud, deepfake onboarding).

• It doesn’t equip teams to think across functions like AML, cyber, fraud, tech, and risk all intersect in modern cases.

  
  

What Future‑Ready AML Training Should Cover

If you’re designing or choosing a program, it should include:

• Live or simulated AI-enabled case studies, cover deepfakes, synthetic IDs, crypto laundering, insider threats.

• Cross-functional modules, blending cyber awareness, fraud logic, and AML typology.

• Tech literacy for compliance officers, understanding data poisoning, model risk, vendor oversight, and AI governance.

• Scenario-based exercises to spot anomalies, escalate risk, and communicate across teams.

• Support for vendor management training, considering increasing third‑party AI services.

  
  

Why This Matters to You

• Your teams need to be prepared before a regulator or enforcement action singles them out for gaps in AI or cyber readiness.

• You're competing for stakeholder trust, demonstrating proactive adaptation to new risks builds credibility in audits or inspections.

• Modern AML training isn’t just compliance, its culture, agility, and cross-disciplinary intelligence.

  
  

In Summary

The intersection of AI, cyber risk, and money laundering represents a pivotal moment in financial crime prevention. As criminals adopt GenAI at scale, compliance training must evolve, from outdated checklists to dynamic, tech-savvy readiness.

Let’s talk about how tailored, scenario-based training can equip your compliance teams with the mindset, tools, and vigilance needed for today, and tomorrow.