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Course Outline
Introduction to AI in Combating Financial Crime
- The landscape of fraud and AML in the age of digital finance
- Contrasting traditional methods with AI-driven solutions
- Real-world case studies from Mastercard, JPMorgan, and other global banks
Machine Learning for Transaction Oversight
- Applying supervised learning to risk scoring and categorization
- Using unsupervised learning to spot anomalies
- Generating real-time alerts through stream processing
Graph Analytics and Identifying Network Risks
- Mapping relationships between various entities and transactions
- Identifying intricate fraud schemes via graph AI
- Practical sessions using Neo4j or comparable tools
Natural Language Processing for AML Purposes
- Applying text mining to customer due diligence (CDD)
- Conducting watchlist scans with named entity recognition (NER)
- Utilizing prompt-based techniques for document review and suspicious activity reports (SARs)
Model Governance and Interpretability
- Constructing models that are both explainable and auditable
- Identifying and mitigating bias in fraud detection algorithms
- Implementing XAI techniques within compliance contexts
Ethics, Regulatory Compliance, and Model Risk
- Adhering to AML and KYC frameworks (such as FATF, FinCEN, and EBA)
- Navigating AI ethics in surveillance and customer monitoring
- Meeting reporting standards and ensuring regulatory auditability
Deployment Strategies and Emerging Trends
- Integrating AI models into established transaction systems
- Establishing feedback loops and mechanisms for model updates
- The role of generative AI in future fraud investigations and SAR automation
Conclusions and Future Pathways
Requirements
- Foundational knowledge of fraud risks and AML protocols
- Background experience in data analysis or compliance reporting
- Basic proficiency with Python or standard analytics platforms
Target Audience
- Fraud risk specialists
- AML compliance teams
- Security managers
14 Hours
Testimonials (1)
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