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Course Outline
Introduction to AI in Financial Crime Prevention
- Contextualizing fraud and AML challenges in the era of digital finance
- Comparing conventional methods with AI-driven solutions
- Analyzing case studies from Mastercard, JPMorgan, and major global banks
Machine Learning for Transaction Surveillance
- Applying supervised learning techniques for risk scoring and data classification
- Using unsupervised learning to identify anomalies
- Managing real-time alert creation and stream data processing
Graph Analytics for Network Risk Identification
- Creating models that map relationships between entities and transactions
- Identifying intricate fraud patterns through graph AI
- Practical exercises using Neo4j or comparable graph databases
Natural Language Processing in AML Contexts
- Applying text mining techniques to customer due diligence (CDD)
- Enhancing watchlist screening via named entity recognition (NER)
- Leveraging prompt-based AI for document analysis and suspicious activity reporting (SARs)
Model Governance and Transparency
- Constructing models that are both explainable and audit-ready
- Identifying and addressing bias in fraud detection algorithms
- Implementing XAI techniques within compliance frameworks
Ethics, Regulatory Compliance, and Model Risk
- Adhering to AML and KYC standards (including FATF, FinCEN, and EBA guidelines)
- Navigating ethical considerations in AI surveillance and customer oversight
- Meeting reporting benchmarks and ensuring regulatory auditability
Deployment Strategies and Emerging Trends
- Embedding AI models into current transactional infrastructure
- Establishing feedback loops and continuous model refinement processes
- Exploring the role of generative AI in fraud investigation and SAR automation
Conclusion and Path Forward
Requirements
- Solid comprehension of fraud risk factors and AML protocols
- Practical experience in data analytics or compliance reporting
- Foundational knowledge of Python or leading analytics platforms
Target Audience
- Specialists in fraud risk management
- AML compliance professionals
- Security administrators
14 Hours
Testimonials (1)
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