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Course Outline

Introduction to AI in Financial Crime

  • Overview of fraud and AML challenges in digital finance
  • Comparison of traditional methods versus AI-driven approaches
  • Real-world case studies from Mastercard, JPMorgan, and other 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 data streams

Graph Analytics and Network Risk Detection

  • Mapping relationships between entities and transactions
  • Identifying complex fraud patterns through graph AI
  • Hands-on experience with Neo4j or comparable tools

Natural Language Processing for AML

  • Conducting text mining for customer due diligence (CDD)
  • Scanning watchlists using named entity recognition (NER)
  • Reviewing documents and suspicious activity reports (SARs) via prompts

Model Governance and Explainability

  • Developing models that are explainable and auditable
  • Detecting and mitigating bias in fraud detection algorithms
  • Applying XAI techniques within compliance frameworks

Ethics, Regulation, and Model Risk

  • Adhering to AML and KYC frameworks (e.g., FATF, FinCEN, EBA)
  • Addressing AI ethics in surveillance and customer monitoring
  • Meeting reporting standards and ensuring regulatory auditability

Deployment Strategies and Emerging Trends

  • Integrating AI models into current transaction systems
  • Implementing feedback loops and mechanisms for model updates
  • Exploring the future role of generative AI in fraud investigations and SAR automation

Summary and Future Directions

Requirements

  • Knowledge of fraud risk and AML protocols
  • Background in data analysis or compliance reporting
  • Foundational understanding of Python or analytics platforms

Target Audience

  • Professionals in fraud risk management
  • AML compliance teams
  • Security managers
 14 Hours

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