AI for Fraud Detection & Anti‑Money Laundering Training Course

Introduction to AI in Financial Crime

• Overview of fraud and AML landscapes in the digital finance era
• Comparing traditional methods with AI-driven solutions
• Case studies involving Mastercard, JPMorgan, and global banking institutions

Machine Learning for Transaction Monitoring

• Applying supervised learning for risk scoring and classification
• Using unsupervised learning techniques for anomaly detection
• Generating real-time alerts through stream processing

Graph Analytics and Network Risk Detection

• Modeling connections between entities and financial transactions
• Identifying intricate fraud schemes using graph AI
• Practical sessions using Neo4j or comparable tools

Natural Language Processing for AML

• Text mining applications in customer due diligence (CDD)
• Watchlist scanning via named entity recognition (NER)
• Prompt-based document analysis and preparation of suspicious activity reports (SARs)

Model Governance and Explainability

• Developing models that are explainable and subject to audit
• Identifying and mitigating bias in fraud detection algorithms
• Implementing XAI techniques within compliance frameworks

Ethics, Regulation, and Model Risk

• Ensuring adherence to AML and KYC frameworks (e.g., FATF, FinCEN, EBA)
• Considering AI ethics in surveillance and customer monitoring practices
• Maintaining reporting standards and regulatory auditability

Deployment Strategies and Future Trends

• Integrating AI models into existing transaction processing systems
• Establishing feedback loops and model update mechanisms
• Exploring the role of generative AI in fraud investigation and SAR automation

Summary and Next Steps