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Course Outline
Introduction to AI in Financial Crime
- Fraud and AML landscape in the digital finance era
- Conventional methods versus AI-driven solutions
- Case studies from Mastercard, JPMorgan, and international banks
Machine Learning for Transaction Monitoring
- Supervised learning for risk assessment and classification
- Unsupervised learning for identifying anomalies
- Real-time alert creation and stream data processing
Graph Analytics and Network Risk Detection
- Modelling connections between entities and transactions
- Identifying intricate fraud schemes via graph AI
- Practical work with Neo4j or equivalent tools
Natural Language Processing for AML
- Text mining for customer due diligence (CDD)
- Watchlist scanning through named entity recognition (NER)
- Prompt-driven document analysis and suspicious activity reports (SARs)
Model Governance and Explainability
- Creating models that are explainable and subject to audit
- Identifying and mitigating bias in fraud detection algorithms
- Applying XAI techniques in compliance environments
Ethics, Regulation, and Model Risk
- Adherence to AML and KYC frameworks (e.g., FATF, FinCEN, EBA)
- AI ethics in surveillance and customer oversight
- Reporting standards and regulatory audit trails
Deployment Strategies and Future Trends
- Incorporating AI models into established transaction systems
- Feedback mechanisms and model refinement processes
- The role of generative AI in fraud investigations and SAR automation
Summary and Next Steps
Requirements
- Familiarity with fraud risk and AML procedures
- Prior experience in data analysis or compliance reporting
- Foundational 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
