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

Introduction to Generative AI

• An overview of generative models and their significance within the finance sector
• Classifications of generative models: LLMs, GANs, and VAEs
• Advantages and constraints within financial environments

Applying Generative Adversarial Networks (GANs) in Finance

• Understanding GAN mechanics: the interplay between generators and discriminators
• Utilising GANs for synthetic data creation and fraud simulation
• Case study: producing realistic transaction data for testing purposes

Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and generate financial text
• Formulating prompts for forecasting and risk assessment
• Practical applications: summarising financial reports, KYC processes, and detecting red flags

Financial Forecasting with Generative AI

• Implementing time series forecasting using hybrid LLM and ML models
• Creating scenarios and conducting stress tests
• Use case: predicting revenue by integrating structured and unstructured data

Fraud Detection and Anomaly Identification

• Employing GANs to detect anomalies in transaction data
• Identifying emerging fraud patterns via LLM workflows based on prompt engineering
• Evaluating models: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in generative AI outputs
• Mitigating risks associated with model hallucinations and bias in finance
• Adhering to 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 and risk/compliance obligations
• Establishing governance frameworks for responsible AI deployment

Conclusion and Future Directions