AI TRAINING
AI for Financial Crime, AML and KYC Teams
Equip compliance and fraud teams to evaluate, deploy, and govern AI in financial crime detection workflows.
What it covers
This practitioner-level programme covers the end-to-end application of AI and machine learning in anti-money laundering, know-your-customer processes, and fraud detection. Participants learn how modern transaction monitoring systems work, how entity resolution and graph analytics surface hidden networks, and how to assess vendor claims against regulatory expectations from the EBA, FinCEN, and FATF. The programme combines case studies, hands-on vendor evaluation exercises, and a SAR quality improvement workshop. Delivery mixes instructor-led sessions with applied labs using anonymised typology data.
What you'll be able to do
- Critically evaluate an AI transaction monitoring vendor's model card against EBA and FATF explainability expectations
- Map a money laundering typology to the appropriate graph analytic or ML detection technique
- Design a SAR quality improvement workflow using AI-assisted narrative generation with human review gates
- Build a vendor RFP scorecard that stress-tests false-positive rates, model drift policies, and audit trail requirements
- Articulate the model risk management obligations (SR 11-7 / SS1/23) applicable to an AI fraud model in production
Topics covered
- How ML-based transaction monitoring differs from rule-based systems and when each is appropriate
- Entity resolution and graph analytics for uncovering hidden beneficial ownership and money mule networks
- AI-assisted SAR drafting: improving narrative quality, reducing false positives, and meeting regulatory expectations
- KYC automation: document verification, biometric checks, and continuous monitoring with AI
- Regulatory expectations on explainable AI from EBA, FATF, FCA, and FinCEN model risk guidance
- Vendor landscape assessment: evaluating NICE Actimize, Quantexa, Feedzai, Napier, and open-source alternatives
- Model risk management (SR 11-7 / SS1/23) applied to AML and fraud models
- Bias, fairness, and adverse-action obligations in automated financial crime decisions
Delivery
Delivered as a blended programme over three to four weeks: two live instructor-led full-day sessions (remote or on-site) bookend four facilitated 90-minute online workshops. Roughly 60% of time is hands-on — vendor evaluation exercises, anonymised case datasets, and a capstone SAR workflow redesign. Participants receive a typology reference pack, a vendor assessment template, and access to a private cohort Slack channel for peer discussion.
What makes it work
- Involve compliance, IT, and internal audit jointly from day one so model governance responsibilities are clear before go-live
- Run a structured parallel-run period (at least 90 days) comparing AI alerts against legacy rule outputs before decommissioning old logic
- Establish a documented false-positive review loop that feeds back into model retraining cadences and is visible to regulators
- Secure explicit regulatory engagement (pre-application meetings with FCA, DNB, or BaFin) when deploying novel AI in high-risk detection scenarios
Common mistakes
- Deploying AI transaction monitoring as a direct rule-engine replacement without establishing a parallel-run validation period, leading to regulatory model risk findings
- Treating explainability as a checkbox rather than designing audit-ready reason codes from the outset, creating SAR defensibility problems
- Underestimating data quality issues — incomplete beneficial ownership data and inconsistent name formats degrade entity resolution accuracy significantly
- Selecting vendors purely on false-negative reduction without negotiating access to model documentation needed for internal validation under SR 11-7
When NOT to take this
This programme is not appropriate for a team that has not yet implemented a baseline AML transaction monitoring system — foundational AML process design and regulatory literacy must come first before AI augmentation adds value.
Providers to consider
Sources
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