AI for Fraud Detection & Anti‑Money Laundering Training Course

Fundamentals of AI in Financial Crime Prevention

• The landscape of fraud and AML in the age of digital finance
• Comparing conventional methods with AI-driven strategies
• Real-world case studies featuring Mastercard, JPMorgan, and other global banking institutions

Machine Learning Applications in Transaction Monitoring

• Supervised learning techniques for risk scoring and classification tasks
• Unsupervised learning methods focused on identifying anomalies
• Generating real-time alerts through stream processing capabilities

Graph Analytics for Network Risk Identification

• Mapping connections between entities and their transactional flows
• Uncovering intricate fraud schemes through graph AI methodologies
• Practical exercises using Neo4j or comparable graph databases

Natural Language Processing in AML Workflows

• Applying text mining techniques to customer due diligence (CDD) processes
• Enhancing watchlist scanning via named entity recognition (NER)
• Utilizing prompt-based techniques for document review and drafting suspicious activity reports (SARs)

Model Governance and Explainability Practices

• Constructing models that are both explainable and subject to audit
• Identifying and mitigating bias within fraud detection algorithms
• Implementing XAI (Explainable AI) techniques to meet compliance requirements

Ethical Considerations, Regulations, and Model Risk

• Aligning with AML and KYC frameworks, including FATF, FinCEN, and EBA guidelines
• Navigating AI ethics in customer surveillance and monitoring activities
• Adhering to reporting standards and ensuring regulatory auditability

Deployment Strategies and Emerging Trends

• Seamlessly integrating AI models into current transaction processing systems
• Establishing feedback loops and mechanisms for continuous model updates
• Exploring the role of generative AI in fraud investigations and SAR automation

Course Recap and Recommended Next Steps