Generative AI in Finance: Forecasting, Fraud & Regulation Training Course

Introduction to Generative AI

• Overview of generative models and their strategic relevance in finance
• Exploration of model types: LLMs, GANs, and VAEs
• Analyzing strengths and constraints within financial applications

Leveraging Generative Adversarial Networks (GANs) in Finance

• Mechanics of GANs: the interplay between generators and discriminators
• Utilizing GANs for synthetic data creation and fraud simulation scenarios
• Case study: creating realistic transaction datasets for testing purposes

Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and produce financial narratives
• Crafting effective prompts for forecasting and risk assessment
• Practical applications: summarizing financial reports, KYC processes, and detecting red flags

Financial Forecasting via Generative AI

• Time-series forecasting using hybrid LLM and machine learning models
• Generating scenarios and conducting stress tests
• Use case: predicting revenue by integrating structured and unstructured data sources

Fraud Detection and Anomaly Identification

• Deploying GANs to spot anomalies in transactional data
• Uncovering emerging fraud patterns through LLM-driven, prompt-based workflows
• Evaluating model performance: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in AI-generated outputs
• Addressing risks related to model hallucinations and bias in financial settings
• Aligning with regulatory standards (e.g., GDPR, Basel guidelines)

Structuring Generative AI Applications for Financial Institutions

• Formulating compelling business cases for internal adoption
• Striking a balance between technological innovation and risk/compliance requirements
• Establishing governance frameworks for responsible AI deployment

Recap and Future Directions