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Course Outline

Introduction to Generative AI

  • Overview of generative models and their significance to the financial sector
  • Types of generative models: LLMs, GANs, VAEs
  • Strengths and limitations in financial contexts

Generative Adversarial Networks (GANs) for Finance

  • Mechanics of GANs: generators versus discriminators
  • Applications in synthetic data generation and fraud simulation
  • Case study: producing realistic transaction data for testing purposes

Large Language Models (LLMs) and Prompt Engineering

  • How LLMs interpret and generate financial text
  • Creating prompts for forecasting and risk analysis
  • Use cases: summarizing financial reports, KYC processes, and red flag detection

Financial Forecasting with Generative AI

  • Time series forecasting using hybrid LLM and ML models
  • Scenario generation and stress testing
  • Use case: revenue prediction leveraging structured and unstructured data

Fraud Detection and Anomaly Identification

  • Employing GANs for transaction anomaly detection
  • Spotting emerging fraud patterns via prompt-based LLM workflows
  • Model evaluation: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Implications

  • Explainability and transparency in generative AI outputs
  • Risks of model hallucination and bias in finance
  • Adherence to regulatory expectations (e.g., GDPR, Basel guidelines)

Designing Generative AI Use Cases for Financial Institutions

  • Developing business cases for internal adoption
  • Balancing innovation with risk management and compliance
  • Governance frameworks for responsible AI deployment

Summary and Next Steps

Requirements

  • A basic understanding of finance and risk management principles
  • Experience with spreadsheets or fundamental data analysis
  • Familiarity with Python is beneficial but not mandatory

Audience

  • Risk managers
  • Compliance analysts
  • Financial auditors
 14 Hours

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