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

Foundations of Generative AI

• Overview of generative models and their significance in the financial sector
• Exploration of model types: LLMs, GANs, and VAEs
• ​Analyzing strengths and constraints within financial applications

Leveraging Generative Adversarial Networks (GANs) in Finance

• Understanding the mechanics of GANs: the interplay between generators and discriminators
• ​Application in creating synthetic data and simulating fraud scenarios
• Case study: Producing realistic transaction datasets for testing purposes

Large Language Models (LLMs) and the Art of Prompting

• How LLMs process and generate financial narratives
• Engineering prompts tailored for forecasting and risk assessment
• Practical applications: Summarizing financial reports, Know Your Customer (KYC) processes, and identifying red flags

Advanced Financial Forecasting via Generative AI

• Applying hybrid LLM and machine learning models for time series prediction
• Generating scenarios and conducting stress tests
• Use case: Forecasting revenue by integrating both structured and unstructured data sources

Detecting Fraud and Identifying Anomalies

• Employing GANs to detect unusual patterns in transaction data
• Discovering emerging fraud trends using LLM-driven prompt workflows
• Assessing model performance: 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 inherent biases in finance
• ​Aligning with regulatory standards (such as GDPR and Basel guidelines)

Developing Generative AI Strategies for Financial Institutions

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

Course Recap and Future Directions