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Course Outline
Introduction to AI in Financial Crime
- The landscape of fraud and AML in the digital finance age
- Comparing traditional methods with AI-driven solutions
- Case studies featuring Mastercard, JPMorgan, and international banks
Machine Learning for Transaction Monitoring
- Supervised learning techniques for risk scoring and classification
- Unsupervised learning approaches for anomaly detection
- Real-time alert generation and stream processing workflows
Graph Analytics and Network Risk Detection
- Mapping relationships between entities and transactions
- Uncovering complex fraud schemes through graph AI
- Practical exercises using Neo4j or comparable tools
Natural Language Processing for AML
- Applying text mining to customer due diligence (CDD)
- Watchlist screening via named entity recognition (NER)
- Prompt-based document analysis and suspicious activity reports (SARs)
Model Governance and Explainability
- Constructing explainable and auditable models
- Identifying and mitigating bias in fraud detection algorithms
- Implementing XAI techniques in compliance environments
Ethics, Regulation, and Model Risk
- Adherence to AML and KYC frameworks (such as FATF, FinCEN, and EBA)
- Ethical considerations in surveillance and customer monitoring
- Reporting standards and ensuring regulatory auditability
Deployment Strategies and Future Trends
- Embedding AI models into current transaction systems
- Establishing feedback loops and model update mechanisms
- The role of generative AI in fraud investigation and SAR automation
Summary and Next Steps
Requirements
- A solid grasp of fraud risks and AML procedures
- Prior experience in data analysis or compliance reporting
- Fundamental knowledge of Python or analytics platforms
Target Audience
- Fraud risk specialists
- AML compliance teams
- Security managers
14 Hours
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
