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Course Outline
Introduction to AI in Financial Crime
- An overview of fraud and AML in the age of digital finance.
- Comparing traditional methods with AI-driven approaches.
- Case studies featuring Mastercard, JPMorgan, and other global banks.
Machine Learning for Transaction Monitoring
- Applying unsupervised learning to identify anomalies.
- Real-time alert generation and stream processing.
Graph Analytics and Network Risk Detection
- Modelling the relationships between entities and transactions.
- Identifying intricate fraud schemes through graph AI.
- Practical work with Neo4j or comparable tools.
Natural Language Processing for AML
- Text mining applications in customer due diligence (CDD).
- Watchlist scanning leveraging named entity recognition (NER).
- Prompt-based document review and suspicious activity reports (SARs).
Model Governance and Explainability
- Creating models that are both explainable and auditable.
- Detecting and mitigating bias in fraud detection algorithms.
- Implementing XAI techniques in compliance environments.
Ethics, Regulation, and Model Risk
- Adhering to AML and KYC frameworks (such as FATF, FinCEN, and EBA).
- Reporting standards and ensuring regulatory auditability.
Deployment Strategies and Future Trends
- Integrating AI models into current transaction systems.
- Establishing feedback loops and model update mechanisms.
Summary and Next Steps
Requirements
- Knowledge of fraud risks and AML procedures.
- Background in data analysis or compliance reporting.
- Fundamental 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
