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

Introduction to Generative AI

• Overview of generative models and their significance in the financial sector
• Varieties of generative models: LLMs, GANs, and VAEs
• Key strengths and constraints in financial applications

Applying Generative Adversarial Networks (GANs) in Finance

• Mechanisms of GANs: the role of generators versus discriminators
• Use in creating synthetic datasets and simulating fraud scenarios
• Case study: producing realistic transaction records for testing purposes

Leveraging Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and produce financial narratives
• Constructing prompts for predictive analysis and risk assessment
• Applications: summarizing financial reports, KYC procedures, and spotting red flags

Financial Forecasting via Generative AI

• Time-series prediction using integrated LLM and machine learning models
• Creating scenarios and conducting stress tests
• Practical example: forecasting revenue by combining structured and unstructured data

Identifying Fraud and Anomalies

• Employing GANs to detect irregularities in transaction patterns
• Uncovering emerging fraud trends using LLM-driven, prompt-based workflows
• Evaluating models: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

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

Developing Generative AI Solutions for Financial Institutions

• Formulating business cases for internal implementation
• Balancing technological innovation with risk management and compliance
• Establishing governance frameworks for responsible AI adoption

Recap and Path Forward