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
• Classifications of generative models, including LLMs, GANs, and VAEs
• Key strengths and constraints within financial environments

Applying Generative Adversarial Networks (GANs) in Finance

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

Large Language Models (LLMs) and Prompt Engineering

• Understanding how LLMs process and create financial text
• Structuring prompts for forecasting and risk assessment
• Practical applications: summarizing financial reports, KYC processes, and identifying red flags

Financial Forecasting Using Generative AI

• Time series forecasting through hybrid LLM and machine learning models
• Generating scenarios for stress testing
• Application example: predicting revenue using both structured and unstructured data

Fraud Detection and Anomaly Identification

• Leveraging GANs to detect anomalies in transaction data
• Recognizing emerging fraud patterns via prompt-driven LLM workflows
• Assessing model performance: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

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

Developing Generative AI Solutions for Financial Institutions

• Constructing business cases for internal adoption
• Striking a balance between innovation and risk/compliance obligations
• Establishing governance frameworks for responsible AI implementation

Recap and Future Directions