Introduction
This course focuses on strengthening Anti-Money Laundering and Counter-Terrorist Financing (AML/CFT) frameworks through the use of advanced analytics and AI technologies. It aligns with international regulatory standards and emphasizes risk-based approaches, transaction monitoring optimization, and effective reporting.
General Objectives
By the end of the course, participants will be able to:
- Enhance understanding of AML/CFT risk management frameworks
- Improve transaction monitoring and detection capabilities
- Strengthen regulatory compliance and reporting effectiveness
- Interpret and apply FATF Recommendations
- Conduct AML/CFT risk assessments for customers, products, and channels
- Apply advanced analytics and AI to transaction monitoring
- Reduce false positives and improve alert management
- Prepare high-quality Suspicious Transaction/Activity Reports (STR/SAR)
Course Outline :
Day 1 – AML/CFT Regulatory Framework
- Global AML/CFT landscape
- FATF Recommendations overview
- Role of regulators and Financial Intelligence Units (FIUs)
- Risk-Based Approach (RBA)
- AML governance and compliance structure
Day 2 – AML/CFT Risk Assessment
- Identifying AML/CFT risk factors
- Customer, product, geography, and channel risks
- Enterprise-wide AML risk assessment
- Risk scoring methodologies
- Documentation and regulatory expectations
Day 3 – Customer Due Diligence (CDD & EDD)
- KYC principles
- Customer risk classification
- Enhanced Due Diligence (EDD)
- Politically Exposed Persons (PEPs)
- Ongoing customer monitoring
Day 4 – Transaction Monitoring Systems
- Traditional rules-based monitoring
- Thresholds and scenarios
- System limitations and challenges
- Alert generation workflows
- Regulatory expectations for monitoring systems
Day 5 – Advanced Analytics in AML
- Data analytics techniques in AML
- Behavioral profiling
- Network and link analysis
- Data visualization for investigations
- Improving detection effectiveness
Day 6 – AI & Machine Learning in AML
- Machine learning for suspicious activity detection
- Supervised vs. unsupervised AML models
- Use of AI in typology detection
- Model training and tuning
- Managing model risk in AML systems
Day 7 – Alert Management & False Positive Reduction
- Causes of false positives
- Alert prioritization techniques
- AI-driven alert optimization
- Investigator workflows
- Performance metrics (precision, recall)
Day 8 – STR/SAR Reporting
- Suspicious Transaction/Activity Reports
- Quality and completeness requirements
- Red flags and typologies
- Communication with FIUs
- Regulatory consequences of poor reporting
Day 9 – Global Case Studies & Enforcement Actions
- Major AML enforcement cases
- Root causes of AML failures
- Lessons learned from penalties
- Regulatory expectations post-fines
- Strengthening AML control frameworks
Day 10 – Practical AML Workshop & Assessment
- End-to-end AML case simulation
- Investigation and decision-making exercise
- Drafting a STR/SAR
- AML system improvement roadmap
- Final assessment and feedback