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
• Exploring different model types: LLMs, GANs, and VAEs
• Analyzing strengths and constraints within financial environments

Applying Generative Adversarial Networks (GANs) in Finance

• Understanding the mechanics of GANs: the role of generators versus 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

• How LLMs process and produce financial text
• Crafting prompts for forecasting and risk assessment
• Key applications: summarizing financial reports, KYC processes, and identifying red flags

Financial Forecasting using Generative AI

• Time series forecasting through hybrid LLM and ML model architectures
• Generating scenarios and conducting stress tests
• Use case: predicting revenue by leveraging both structured and unstructured data

Fraud Detection and Anomaly Identification

• Leveraging GANs to detect anomalies in transaction data
• Identifying emerging fraud patterns using LLM-based prompt workflows
• Evaluating models: distinguishing between false positives and genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in generative AI outputs
• Addressing risks related to model hallucination and bias in finance
• Maintaining compliance with regulatory standards (e.g., GDPR, Basel guidelines)

Designing Generative AI Solutions for Financial Institutions

• Developing business cases to support internal adoption
• Balancing technological innovation with risk and compliance requirements
• Establishing governance frameworks for responsible AI deployment

Summary and Recommended Next Steps