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

Introduction to Generative AI

• Overview of generative models and their significance in the financial sector
• Key model types: LLMs, GANs, and VAEs
• Advantages and constraints within financial applications

Applying Generative Adversarial Networks (GANs) to Finance

• The mechanics of GANs: the role of generators versus discriminators
• Uses in creating synthetic data and simulating fraud scenarios
• Case study: producing realistic transaction data for testing purposes

Large Language Models (LLMs) and Prompt Engineering

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

Financial Forecasting Using Generative AI

• Time-series forecasting using hybrid LLM and machine learning models
• Generating scenarios and conducting stress tests
• Application example: predicting revenue by leveraging both structured and unstructured data

Fraud Detection and Anomaly Identification

• Employing GANs to detect anomalies in transactional data
• Recognizing emerging fraud patterns via LLM-based 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 (e.g., GDPR, Basel guidelines)

Developing Generative AI Use Cases for Financial Institutions

• Establishing business cases for internal adoption
• Striking a balance between innovation, risk management, and compliance
• Implementing governance frameworks for responsible AI deployment

Conclusion and Future Directions