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

Introduction to Generative AI

• Overview of generative models and their significance in the financial sector.
• Categories of generative models: LLMs, GANs, and VAEs.
• Advantages and constraints in financial applications.

Applying Generative Adversarial Networks (GANs) to Finance

• Mechanics of GANs: the interplay between generators and discriminators.
• Applications in creating synthetic data and simulating fraud scenarios.
• Case study: Producing realistic transaction data for testing purposes.

Large Language Models (LLMs) and the Art of Prompt Engineering

• How LLMs process and generate financial text.
• Structuring prompts for forecasting and risk assessment.
• Practical uses: Summarizing financial reports, KYC processes, and detecting red flags.

Financial Forecasting Leveraging Generative AI

• Time series forecasting using hybrid LLM and machine learning models.
• Creating scenarios and conducting stress tests.
• Use case: Predicting revenue by integrating structured and unstructured data.

Fraud Detection and Anomaly Identification

• Leveraging GANs to spot anomalies in transactional data.
• Uncovering emerging fraud patterns via prompt-driven LLM workflows.
• Model assessment: Distinguishing false positives from genuine risk indicators.

Regulatory and Ethical Considerations

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

Crafting Generative AI Use Cases for Financial Institutions

• Developing business cases for internal adoption.
• Balancing technological innovation with risk management and compliance.
• Implementing governance frameworks for responsible AI deployment.

Conclusion and Future Directions