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Course Outline
Introduction to AI in Financial Crime
- Overview of fraud and AML in the era of digital finance.
- Comparing traditional methods with AI-based approaches.
- Case studies from Mastercard, JPMorgan, and global banks.
Machine Learning for Transaction Monitoring
- Applying supervised learning for risk scoring and classification.
- Using unsupervised learning for anomaly detection.
- Generating real-time alerts and processing streams.
Graph Analytics and Network Risk Detection
- Modelling relationships between entities and transactions.
- Identifying complex fraud schemes using graph AI.
- Practical application using Neo4j or similar tools.
Natural Language Processing for AML
- Text mining in customer due diligence (CDD).
- Watchlist scanning using named entity recognition (NER).
- Prompt-based document review and suspicious activity reports (SARs).
Model Governance and Explainability
- Creating explainable and auditable models.
- Detecting and mitigating bias in fraud detection algorithms.
- Implementing XAI techniques in compliance environments.
Ethics, Regulation, and Model Risk
- Ensuring compliance with AML and KYC frameworks (e.g. FATF, FinCEN, EBA).
- Ethical considerations in AI surveillance and customer monitoring.
- Maintaining reporting standards and regulatory auditability.
Deployment Strategies and Future Trends
- Integrating AI models into existing transaction systems.
- Establishing feedback loops and model updating mechanisms.
- The role of generative AI in fraud investigation and SAR automation.
Summary and Next Steps
Requirements
- A solid understanding of fraud risks and AML procedures.
- Practical experience in data analysis or compliance reporting.
- Foundational familiarity with 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
