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Course Outline

Introduction to Generative AI

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

Generative Adversarial Networks (GANs) for Finance

  • Understanding GAN mechanics: the interplay between generators and discriminators
  • Applications in generating synthetic data and simulating fraud scenarios
  • Case study: creating realistic transaction data for system testing

Large Language Models (LLMs) and Prompt Engineering

  • How LLMs process and generate financial text
  • Designing prompts for forecasting and risk analysis tasks
  • Use cases: summarizing financial reports, Know Your Customer (KYC) processes, and detecting red flags

Financial Forecasting with Generative AI

  • Time series forecasting using hybrid models that combine LLMs with traditional machine learning
  • Generating scenarios and conducting stress tests
  • Use case: predicting revenue by analyzing both structured and unstructured data

Fraud Detection and Anomaly Identification

  • Utilizing GANs to detect anomalies in transactional data
  • Identifying emerging fraud patterns through prompt-based LLM workflows
  • Model evaluation: distinguishing between false positives and genuine risk indicators

Regulatory and Ethical Implications

  • Ensuring explainability and transparency in generative AI outputs
  • Mitigating risks related to model hallucination and bias in financial applications
  • Ensuring compliance with regulatory standards (e.g., GDPR, Basel guidelines)

Designing Generative AI Use Cases for Financial Institutions

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

Summary and Next Steps

Requirements

  • A solid understanding of core finance and risk management principles
  • Practical experience with spreadsheets or basic data analysis techniques
  • Familiarity with Python is beneficial but not mandatory

Target Audience

  • Risk managers
  • Compliance analysts
  • Financial auditors
 14 Hours

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