Get in Touch

Course Outline

Introduction to Generative AI

  • Overview of generative models and their importance in finance.
  • Overview of generative model types: LLMs, GANs, and VAEs.
  • Strengths and limitations within financial contexts.

Generative Adversarial Networks (GANs) for Finance

  • Mechanism of GANs: distinguishing between generators and discriminators.
  • Applications in creating synthetic data and simulating fraud scenarios.
  • Case study: Generating realistic transaction data for testing purposes.

Large Language Models (LLMs) and Prompt Engineering

  • Understanding how LLMs process and generate financial text.
  • Developing prompts for forecasting and risk analysis.
  • Practical use cases: summarizing financial reports, Know Your Customer (KYC) processes, and detecting red flags.

Financial Forecasting with Generative AI

  • Time series forecasting utilizing hybrid LLM and machine learning models.
  • Scenario generation and stress testing techniques.
  • Use case: Predicting revenue by integrating structured and unstructured data.

Fraud Detection and Anomaly Identification

  • Employing GANs for anomaly detection in transaction data.
  • Spotting emerging fraud patterns through prompt-based LLM workflows.
  • Evaluating model performance: balancing false positives against true risk indicators.

Regulatory and Ethical Implications

  • Ensuring explainability and transparency in generative AI outputs.
  • Addressing risks of model hallucination and bias in financial applications.
  • Aligning 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 requirements.
  • Establishing governance frameworks for the responsible deployment of AI.

Summary and Next Steps

Requirements

  • A foundational understanding of finance and risk management principles.
  • Experience using spreadsheets or performing basic data analysis.
  • While helpful, prior familiarity with Python is not mandatory.

Target Audience

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

Number of participants


Price per participant

Testimonials (1)

Upcoming Courses

Related Categories