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Course Outline

Introduction to AI in Financial Crime

  • Overview of fraud and AML in the digital finance landscape.
  • Comparison of traditional methods versus AI-driven approaches.
  • Case studies featuring Mastercard, JPMorgan, and global banks.

Machine Learning for Transaction Monitoring

  • Supervised learning techniques for risk scoring and classification.
  • Unsupervised learning for detecting anomalies.
  • Real-time alert generation and stream processing.

Graph Analytics and Network Risk Detection

  • Modeling relationships between entities and transactions.
  • Identifying complex fraud schemes using graph AI.
  • Practical session with Neo4j or similar tools.

Natural Language Processing for AML

  • Text mining for customer due diligence (CDD).
  • Watchlist scanning utilizing named entity recognition (NER).
  • Document review and suspicious activity reports (SARs) using prompt-based methods.

Model Governance and Explainability

  • Creating explainable and auditable models.
  • Detecting and mitigating bias in fraud detection algorithms.
  • Applying XAI (Explainable AI) techniques in compliance contexts.

Ethics, Regulation, and Model Risk

  • Adhering to AML and KYC frameworks (e.g., FATF, FinCEN, EBA).
  • Ethical considerations in surveillance and customer monitoring.
  • 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 future role of generative AI in fraud investigation and SAR automation.

Summary and Next Steps

Requirements

  • Knowledge of fraud risk and AML procedures.
  • Experience in data analysis or compliance reporting.
  • Basic familiarity with Python or analytics platforms.

Target Audience

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

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