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Course Outline

Introduction to AI in Financial Crime

  • Overview of fraud and AML in the digital finance era.
  • Comparison of traditional vs AI-based approaches.
  • Case studies from Mastercard, JPMorgan, and global banks.

Machine Learning for Transaction Monitoring

  • Supervised learning for risk scoring and classification.
  • Unsupervised learning for anomaly detection.
  • Real-time alert generation and stream processing.

Graph Analytics and Network Risk Detection

  • Modeling relationships between entities and transactions.
  • Detecting complex fraud schemes using graph AI.
  • Hands-on experience with 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

  • Building explainable and auditable models.
  • Bias detection and mitigation in fraud detection algorithms.
  • Use of XAI techniques in compliance settings.

Ethics, Regulation, and Model Risk

  • Compliance with AML and KYC frameworks (e.g., FATF, FinCEN, EBA).
  • AI ethics in surveillance and customer monitoring.
  • Reporting standards and regulatory auditability.

Deployment Strategies and Future Trends

  • Integrating AI models into existing transaction systems.
  • Feedback loops and model updating mechanisms.
  • Future of generative AI in fraud investigation and SAR automation.

Summary and Next Steps

Requirements

  • A foundational understanding of fraud risk and AML procedures.
  • Experience with data analysis or compliance reporting.
  • Basic familiarity with Python or other analytics platforms.

Audience

  • Fraud risk professionals.
  • AML compliance teams.
  • Security managers.
 14 Hours

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