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

Foundations of Generative AI

• An overview of generative models and their significance in the financial sector.
• Exploration of model types, including LLMs, GANs, and VAEs.
• Analysis of strengths and constraints in financial applications.

Applying Generative Adversarial Networks (GANs) to Finance

• Understanding the mechanics of GANs: the interplay between generators and discriminators.
• Practical uses in synthetic data creation and fraud scenario simulation.
• Case study: Creating realistic transaction datasets for testing purposes.

Large Language Models (LLMs) and Prompt Engineering

• How LLMs process and produce financial textual data.
• Developing prompts tailored for forecasting and risk assessment.
• Key use cases: Summarizing financial reports, Know Your Customer (KYC) processes, and identifying red flags.

Financial Forecasting via Generative AI

• Leveraging hybrid LLM and ML models for time series forecasting.
• Generating scenarios and conducting stress tests.
• Use case: Predicting revenue by integrating structured and unstructured data sources.

Detecting Fraud and Identifying Anomalies

• Utilizing GANs to spot anomalies within transaction flows.
• Uncovering emerging fraud patterns through LLM-based prompt workflows.
• Evaluating models: Distinguishing between false positives and genuine risk indicators.

Regulatory and Ethical Dimensions

• Ensuring explainability and transparency in AI-generated outputs.
• Addressing risks related to model hallucinations and bias in finance.
• Aligning with regulatory standards, such as GDPR and Basel guidelines.

Crafting Generative AI Use Cases for Financial Institutions

• Developing business cases to drive internal adoption.
• Striking a balance between innovation, risk management, and compliance.
• Establishing governance frameworks for the responsible deployment of AI.

Recap and Path Forward