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
• Key strengths and constraints when applied to financial environments

Leveraging Generative Adversarial Networks (GANs) in Finance

• Understanding GAN mechanics: the role of generators versus discriminators
• Case study: creating realistic transaction data for rigorous testing

Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and generate financial documentation
• Structuring prompts for accurate forecasting and risk assessment
• Practical applications: summarising financial reports, KYC processes, and detecting warning signs

Financial Forecasting via Generative AI

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

Detecting Fraud and Identifying Anomalies

• Applying GANs to spot anomalies in transaction streams
• Uncovering emerging fraud trends through LLM-driven prompt workflows
• Model assessment: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Dimensions

• Addressing risks of model hallucination and bias within financial systems
• Ensuring adherence to regulatory standards (e.g., GDPR, Basel guidelines)

Developing Generative AI Use Cases for Financial Institutions

• Constructing business justifications for internal adoption
• Balancing technological innovation with risk management and compliance
• Establishing governance frameworks for responsible AI deployment

Recap and Future Directions