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Course Outline

Introduction to Generative AI

  • Overview of generative models and their significance to finance.
  • Types of generative models: LLMs, GANs, and VAEs.
  • Strengths and limitations within financial contexts.

Generative Adversarial Networks (GANs) for Finance

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

Large Language Models (LLMs) and Prompt Engineering

  • How LLMs process and generate financial text.
  • Developing prompts for forecasting and risk analysis.
  • Use cases: summarizing financial reports, KYC processes, and detecting red flags.

Financial Forecasting with Generative AI

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

Fraud Detection and Anomaly Identification

  • Leveraging GANs for anomaly detection in transactions.
  • Identifying emerging fraud patterns via prompt-based LLM workflows.
  • Model evaluation: distinguishing false positives from true risk indicators.

Regulatory and Ethical Implications

  • Explainability and transparency in generative AI outputs.
  • Risks of model hallucination and bias in financial applications.
  • Compliance with regulatory standards (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 solid understanding of fundamental finance and risk management principles.
  • Experience with spreadsheets or basic data analysis techniques.
  • Familiarity with Python is advantageous but not mandatory.

Target Audience

  • Risk managers.
  • Compliance analysts.
  • Financial auditors.
 14 Hours

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