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

Introduction to Generative AI

• An overview of generative models and their significance in the financial sector.
• Key model types: LLMs, GANs, and VAEs.
• Analyzing the strengths and constraints of these models in financial applications.

Applying Generative Adversarial Networks (GANs) in Finance

• Mechanisms 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.

Leveraging Large Language Models (LLMs) and Prompt Engineering

• How LLMs interpret and generate text relevant to finance.
• Developing prompts for predictive forecasting and risk assessment.
• Applications: Summarizing financial reports, Know Your Customer (KYC) processes, and identifying red flags.

Generative AI for Financial Forecasting

• Combining hybrid LLM and ML models for time series forecasting.
• Generating scenarios and conducting stress tests.
• Application: Forecasting revenue by integrating structured and unstructured data sources.

Fraud Detection and Anomaly Recognition

• Utilizing GANs to identify anomalies within transaction streams.
• Detecting emerging fraud patterns via LLM workflows driven by strategic prompts.
• Model assessment: Distinguishing between false positives and genuine risk indicators.

Regulatory and Ethical Considerations

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

Formulating Generative AI Use Cases for Financial Institutions

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

Conclusion and Future Directions