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Duration 14 hours (2 days)
Course Outline
Introduction to AI in Financial Crime
- Contextualizing fraud and AML challenges in the digital finance landscape
- Comparing traditional methods with AI-driven solutions
- Examining real-world case studies from Mastercard, JPMorgan, and other global banks
Machine Learning for Transaction Monitoring
- Applying supervised learning for risk scoring and classification tasks
- Utilizing unsupervised learning to identify anomalies
- Implementing real-time alert generation and stream processing capabilities
Graph Analytics and Network Risk Detection
- Modeling complex relationships between entities and transaction flows
- Identifying intricate fraud schemes through graph AI techniques
- Practical exercises using Neo4j or comparable tools
Natural Language Processing for AML
- Conducting text mining within Customer Due Diligence (CDD) processes
- Enhancing watchlist scanning using Named Entity Recognition (NER)
- Automating document review and Suspicious Activity Reports (SARs) via prompt-based methods
Model Governance and Explainability
- Developing models that are both explainable and audit-ready
- Detecting and mitigating bias within fraud detection algorithms
- Integrating XAI techniques to support compliance requirements
Ethics, Regulation, and Model Risk
- Ensuring alignment with AML and KYC frameworks, such as FATF, FinCEN, and EBA
- Addressing AI ethical considerations in surveillance and customer monitoring
- Adhering to reporting standards and maintaining regulatory auditability
Deployment Strategies and Future Trends
- Seamlessly integrating AI models into existing transaction systems
- Establishing effective feedback loops and model update mechanisms
- Exploring the role of generative AI in fraud investigation and SAR automation
Summary and Next Steps
Requirements
- A solid understanding of fraud risk principles and AML procedures
- Practical experience in data analysis or compliance reporting
- Foundational knowledge of Python or standard analytics platforms
Target Audience
- Fraud risk professionals
- AML compliance specialists and teams
- Security managers
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
