Course Outline

Introduction to Generative AI

  • Overview of generative models and their relevance to finance
  • Types of generative models: LLMs, GANs, VAEs
  • Strengths and limitations in financial contexts

Generative Adversarial Networks (GANs) for Finance

  • How GANs work: generators vs discriminators
  • Applications in synthetic data generation and fraud simulation
  • Case study: generating realistic transaction data for testing

Large Language Models (LLMs) and Prompt Engineering

  • How LLMs understand and generate financial text
  • Designing prompts for forecasting and risk analysis
  • Use cases: financial report summarization, KYC, red flag detection

Financial Forecasting with Generative AI

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

Fraud Detection and Anomaly Identification

  • Using GANs for anomaly detection in transactions
  • Identifying emerging fraud patterns through prompt-based LLM workflows
  • Model evaluation: false positives vs true risk indicators

Regulatory and Ethical Implications

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

Designing Generative AI Use Cases for Financial Institutions

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

Summary and Next Steps

Requirements

  • An understanding of basic finance and risk management concepts
  • Experience with spreadsheets or basic data analysis
  • Familiarity with Python is helpful but not required

Audience

  • Risk managers
  • Compliance analysts
  • Financial auditors
 14 Hours

Delivery Options

Private Group Training

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  • Delivered online, onsite/classroom or hybrid by experts sharing real world experience

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