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Course Outline
AI Foundations in Financial Crime Prevention
- The landscape of fraud and AML in the digital banking era
- Comparing conventional methods with AI-driven strategies
- Real-world examples from Mastercard, JPMorgan, and other global financial institutions
Applying Machine Learning to Transaction Surveillance
- Using supervised learning for risk assessment and categorization
- Employing unsupervised learning to identify anomalies
- Generating instant alerts and processing data streams
Graph Analysis for Identifying Network Risks
- Mapping connections between entities and financial transactions
- Uncovering intricate fraud patterns through graph AI
- Practical work with Neo4j or equivalent platforms
NLP Applications in AML Compliance
- Text analysis in Customer Due Diligence (CDD) processes
- Scanning watchlists using Named Entity Recognition (NER)
- Leveraging prompts for document review and Suspicious Activity Reports (SARs)
Governance and Model Transparency
- Creating models that are interpretable and audit-ready
- Identifying and addressing bias in fraud detection algorithms
- Integrating XAI methods into compliance operations
Ethical Considerations, Regulations, and Model Risk
- Adhering to AML and KYC standards (such as FATF, FinCEN, EBA)
- Ethical AI practices in monitoring and surveillance
- Meeting reporting requirements and ensuring regulatory auditability
Implementation Strategies and Emerging Trends
- Embedding AI models into current transaction infrastructure
- Establishing feedback cycles and mechanisms for model refinement
- The role of generative AI in fraud investigation and automating SARs
Conclusions and Recommended Next Steps
Requirements
- Familiarity with fraud risk management and AML workflows
- Background in data analysis or compliance reporting
- Foundational knowledge of Python or comparable analytics environments
Target Participants
- Specialists in fraud risk management
- Teams dedicated to AML compliance
- Security administrators
14 Hours
Testimonials (1)
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