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

Fundamentals of Generative AI

• An overview of generative models and their significance in the financial industry
• Categories of generative models, including LLMs, GANs, and VAEs
• Advantages and constraints when applied to financial scenarios

Leveraging Generative Adversarial Networks (GANs) in Finance

• Mechanisms of GANs: the interplay between generators and discriminators
• Practical uses in creating synthetic data and simulating fraud activities
• Case study: producing realistic transaction data for testing purposes

Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and produce financial narratives
• Constructing prompts tailored for forecasting and risk assessment
• Applications: summarizing financial reports, KYC procedures, and detecting red flags

Enhancing Financial Forecasts with Generative AI

• Time series forecasting utilizing hybrid LLM and machine learning models
• Generating scenarios for stress testing
• Use case: predicting revenue by integrating structured and unstructured data

Fraud Detection and Identifying Anomalies

• Employing GANs to spot anomalies in transactional data
• Uncovering new fraud patterns via LLM workflows based on prompt design
• Evaluating models: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in generative AI outputs
• Mitigating risks related to model hallucinations and bias in financial contexts
• Adhering to regulatory standards such as GDPR and Basel guidelines

Developing Generative AI Solutions for Financial Institutions

• Creating business cases to drive internal adoption
• Striking a balance between innovation and risk/compliance obligations
• Establishing governance frameworks for responsible AI deployment

Recap and Future Directions