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

Introduction to Generative AI

• An overview of generative models and their significance to the financial sector
• Classification of generative models: LLMs, GANs, and VAEs
• Analyzing the strengths and constraints within financial applications

Leveraging Generative Adversarial Networks (GANs) in Finance

• Understanding the mechanics of GANs: generators versus discriminators
• Practical uses in creating synthetic data and simulating fraud scenarios
• Case study: Creating realistic transaction data for testing purposes

Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and generate text related to finance
• Structuring prompts for forecasting and risk assessment tasks
• Applications: Summarizing financial reports, KYC processes, and detecting red flags

Applying Generative AI to Financial Forecasting

• Time series forecasting using hybrid models combining LLMs and traditional ML
• Generating scenarios and conducting stress tests
• Use case: Predicting revenue by integrating structured and unstructured data

Fraud Detection and Anomaly Identification

• Employing GANs to spot anomalies in transaction data
• Uncovering emerging fraud patterns via LLM-based prompt workflows
• Evaluating models: Distinguishing false positives from genuine risk indicators

Regulatory and Ethical Dimensions

• Ensuring explainability and transparency in generative AI outputs
• Addressing the risks of model hallucination and bias in financial contexts
• Meeting regulatory standards (e.g., GDPR, Basel guidelines)

Developing Generative AI Use Cases for Financial Institutions

• Constructing business cases for internal adoption
• Balancing technological innovation with risk and compliance requirements
• Implementing governance frameworks for responsible AI deployment

Conclusion and Future Pathways