AI for Fraud Detection & Anti‑Money Laundering Training Course

AI in the Context of Financial Crime

• The landscape of fraud and AML in the age of digital finance
• Comparing traditional methods with AI-driven solutions
• Real-world examples from Mastercard, JPMorgan, and international banking institutions

Applying Machine Learning to Transaction Monitoring

• Supervised learning techniques for risk assessment and classification
• Unsupervised learning methods for identifying anomalies
• Generating real-time alerts and handling data streams

Graph Analytics for Network Risk Identification

• Mapping connections between entities and their transactions
• Uncovering sophisticated fraud schemes through graph AI
• Practical experience with Neo4j and comparable tools

Natural Language Processing in AML

• Applying text mining to customer due diligence (CDD)
• Scan watchlists using named entity recognition (NER)
• Conducting prompt-based document reviews and generating suspicious activity reports (SARs)

Model Governance and Interpretability

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

Ethics, Regulations, and Model Risk Management

• Adhering to AML and KYC regulatory frameworks (such as FATF, FinCEN, and EBA)
• Ethical considerations in surveillance and customer monitoring
• Meeting reporting standards and ensuring regulatory audit readiness

Deployment Strategies and Emerging Trends

• Embedding AI models into current transaction processing systems
• Establishing feedback loops and mechanisms for model updates
• The role of generative AI in fraud investigations and SAR automation

Recap and Recommendations for Next Steps