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

Fundamentals of Generative AI

• An overview of generative models and their strategic relevance to the financial sector
• Classification of generative architectures: LLMs, GANs, and VAEs
• Analyzing the strengths and constraints within financial applications

Application of Generative Adversarial Networks (GANs) in Finance

• Internal mechanics of GANs: the role of generators versus discriminators
• Practical uses in creating synthetic data and simulating fraud scenarios
• Practical case study: creating realistic transaction datasets for testing purposes

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

• How LLMs process and produce financial texts
• Crafting prompts optimized for forecasting and risk assessment
• Practical applications: summarizing financial reports, KYC processes, and identifying red flags

Leveraging Generative AI for Financial Forecasting

• Time series forecasting using hybrid approaches combining LLMs and ML models
• Generating scenarios and conducting stress tests
• Use case: predicting revenue by integrating structured and unstructured data

Fraud Detection and Anomaly Recognition

• Deploying GANs to detect anomalies within transaction flows
• Uncovering emerging fraud patterns via LLM workflows driven by prompts
• Assessing model performance: distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in the outputs of generative AI
• Mitigating risks associated with model hallucinations and bias in financial contexts
• Aligning with regulatory standards (e.g., GDPR, Basel guidelines)

Formulating Generative AI Strategies for Financial Institutions

• Constructing compelling business cases for internal adoption
• Striking a balance between innovation and risk/compliance obligations
• Establishing governance frameworks for the responsible deployment of AI

Recap and Future Directions