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

Introduction to Generative AI

• An overview of generative models and their significance in the financial sector
• Classification of generative models: LLMs, GANs, and VAEs
• Examining the strengths and constraints within financial contexts

Applying Generative Adversarial Networks (GANs) to Finance

• Understanding GAN mechanics: the interplay between generators and discriminators
• Real-world 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
• Crafting prompts for forecasting and risk assessment
• Practical use cases: Summarizing financial reports, KYC processes, and identifying red flags

Financial Forecasting via Generative AI

• Implementing time series forecasting with hybrid LLM and ML models
• Generating scenarios and conducting stress tests
• Use case: Predicting revenue by leveraging both structured and unstructured data

Fraud Detection and Anomaly Identification

• Employing GANs to detect anomalies in transactional data
• Detecting emerging fraud patterns via prompt-based LLM workflows
• Model assessment: Distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

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

Developing Generative AI Use Cases for Financial Institutions

• Constructing business cases to drive internal adoption
• Striking a balance between innovation, risk management, and compliance
• Establishing governance frameworks for the responsible deployment of AI

Wrap-up and Future Directions