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

Introduction to Generative AI

• Overview of generative models and their strategic value in finance
• Classification of generative models: LLMs, GANs, and VAEs
• Analyzing strengths and constraints within financial applications

Leveraging Generative Adversarial Networks (GANs) in Finance

• Mechanics of GANs: The interaction between generators and discriminators
• Practical uses in synthetic data generation and fraud simulation
• Case study: Creating realistic transaction datasets for testing purposes

Large Language Models (LLMs) and Advanced Prompt Engineering

• How LLMs interpret and produce financial textual data
• Structuring prompts for forecasting and risk assessment
• Application examples: Summarizing financial reports, KYC processes, and identifying red flags

Enhancing Financial Forecasting with Generative AI

• Time series forecasting using hybrid LLM and Machine Learning models
• Generating scenarios and conducting stress tests
• Use case: Predicting revenue by integrating structured and unstructured data

Advanced Fraud Detection and Anomaly Identification

• Deploying GANs to detect anomalies in transaction streams
• Uncovering emerging fraud patterns via LLM-driven prompt workflows
• Model validation: Distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

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

Developing Generative AI Strategies for Financial Institutions

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

Conclusion and Future Pathways