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

Fundamentals of Generative AI

• Introduction to generative models and their significance in the financial sector
• Classification of generative models: LLMs, GANs, and VAEs
• Key strengths and constraints when applied to finance

Leveraging Generative Adversarial Networks (GANs) in Finance

• Mechanics of GANs: the interplay between generators and discriminators
• Practical uses 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 Prompting

• How LLMs process and produce financial text
• Structuring prompts for predictive analysis and risk assessment
• Key applications: summarizing financial reports, KYC procedures, and identifying red flags

Advanced Financial Forecasting with Generative AI

• Time series prediction using combined LLM and ML architectures
• Generating scenarios and conducting stress tests
• Application: forecasting revenue by integrating both structured and unstructured data sources

Enhancing Fraud Detection and Anomaly Recognition

• Utilizing GANs to spot anomalies in transactional data
• Detecting novel fraud trends via LLM workflows driven by specific prompts
• Evaluating models: distinguishing false positives from genuine risk signals

Regulatory and Ethical Dimensions

• Ensuring explainability and transparency in generative AI results
• Managing risks related to model hallucinations and bias within finance
• Adhering to regulatory standards (e.g., GDPR, Basel guidelines)

Developing Generative AI Solutions for Financial Institutions

• Formulating business cases for internal integration
• Striking a balance between technological innovation, risk management, and compliance
• Establishing governance structures for responsible AI implementation

Conclusion and Future Directions