AI for Fraud Detection & Anti‑Money Laundering Training Course

Introduction to AI in Financial Crime Prevention

• The landscape of fraud and AML in the digital finance age
• A comparison of traditional methods versus AI-driven solutions
• Real-world case studies from Mastercard, JPMorgan, and other global financial institutions

Machine Learning in Transaction Monitoring

• Applying supervised learning for risk scoring and data classification
• Using unsupervised learning techniques to spot anomalies
• Generating real-time alerts through stream processing

Graph Analytics and Identifying Network Risks

• Mapping relationships between entities and transaction flows
• Uncovering complex fraud schemes through graph AI
• Practical exercises using Neo4j or comparable tools

NLP Applications for AML

• Employing text mining in customer due diligence (CDD)
• Scanning watchlists using named entity recognition (NER)
• Conducting prompt-based document reviews and drafting suspicious activity reports (SARs)

Model Governance and Transparency

• Creating models that are explainable and subject to audit
• Identifying and mitigating bias in fraud detection algorithms
• Applying XAI techniques within compliance contexts

Ethics, Regulatory Frameworks, and Model Risk

• Aligning with AML and KYC frameworks (e.g., FATF, FinCEN, EBA)
• Ethical considerations in surveillance and customer monitoring
• Meeting reporting standards and ensuring regulatory auditability

Deployment Strategies and Emerging Trends

• Embedding AI models into established transaction systems
• Implementing feedback loops and continuous model updating mechanisms
• The role of generative AI in fraud investigations and SAR automation

Recap and Future Directions