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

Foundations of Generative AI

• An introduction to generative models and their significance in the financial sector
• Categories of generative models, including LLMs, GANs, and VAEs
• Analyzing the strengths and constraints of these models in financial settings

Applying Generative Adversarial Networks (GANs) in Finance

• Mechanics of GANs: the interplay between generators and discriminators
• Practical uses in synthetic data creation and fraud simulation
• Case study: Producing realistic transaction data for testing purposes

Large Language Models (LLMs) and Prompt Engineering Strategies

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

Leveraging Generative AI for Financial Forecasting

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

Advanced Fraud Detection and Anomaly Identification

• Employing GANs to detect anomalies in transaction patterns
• Discovering emerging fraud trends through LLM-driven prompt workflows
• Model assessment: Distinguishing false positives from genuine risk indicators

Regulatory and Ethical Considerations

• Ensuring explainability and transparency in generative AI outputs
• Mitigating risks associated with model hallucinations and bias in finance
• Aligning with regulatory standards (such as GDPR and Basel guidelines)

Formulating Generative AI Strategies for Financial Institutions

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

Concluding Remarks and Future Directions