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Course Outline
Introduction to AI in Financial Crime Prevention
- Landscape of fraud and AML challenges in digital finance
- Comparing traditional methods with AI-driven solutions
- Real-world case studies from Mastercard, JPMorgan, and major global banks
Applying Machine Learning to Transaction Monitoring
- Supervised learning techniques for risk scoring and classification
- Unsupervised learning methods for identifying anomalies
- Generating real-time alerts and processing data streams
Graph Analytics for Identifying Network Risks
- Mapping connections between entities and transactions
- Uncovering sophisticated fraud schemes through graph AI
- Practical exercises using Neo4j or comparable tools
NLP Applications in AML Compliance
- Text mining for customer due diligence (CDD)
- Scan watchlists using named entity recognition (NER)
- Automated document review and suspicious activity report (SAR) generation
Model Governance and Explainability
- Creating models that are both explainable and auditable
- Identifying and mitigating bias in fraud detection algorithms
- Integrating XAI techniques into compliance workflows
Ethics, Regulatory Standards, and Model Risk
- Adhering to AML and KYC frameworks (such as FATF, FinCEN, and EBA)
- Ethical considerations in AI-driven surveillance and monitoring
- Meeting reporting standards and ensuring regulatory auditability
Deployment Strategies and Emerging Trends
- Embedding AI models into current transaction infrastructure
- Establishing feedback loops and continuous model updates
- The role of generative AI in fraud investigation and SAR automation
Recap and Recommended Next Steps
Requirements
- A solid grasp of fraud risks and AML protocols
- Prior experience in data analysis or compliance reporting
- Foundational knowledge of Python or common analytics platforms
Target Audience
- Specialists in fraud risk management
- AML compliance teams
- Security administrators
14 Hours
Testimonials (1)
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