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

Introduction to Generative AI

• Understanding generative models and their strategic relevance to the finance sector
• Exploring model types: LLMs, GANs, and VAEs
• Analyzing strengths and limitations within financial applications

Applying Generative Adversarial Networks (GANs) in Finance

• Mechanics of GANs: the interplay between generators and discriminators
• Practical uses in generating 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 financial text
• Developing prompts tailored for forecasting and risk assessment
• Key use cases: summarizing financial reports, KYC processes, and detecting red flags

Advancing Financial Forecasting with Generative AI

• Time-series forecasting utilizing hybrid LLM and ML model approaches
• Generating scenarios and conducting stress tests
• Use case: predicting revenue by integrating structured and unstructured data

Enhancing Fraud Detection and Anomaly Identification

• Leveraging GANs to detect anomalies in transactional data
• Uncovering emerging fraud patterns via LLM-driven, prompt-based workflows
• Evaluating model performance: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in generative AI outputs
• Mitigating risks related to model hallucinations and bias in financial settings
• Aligning with regulatory standards (e.g., GDPR, Basel guidelines)

Developing Generative AI Strategies for Financial Institutions

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

Conclusions and Future Directions