Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Generative AI
- Overview of generative models and their importance in finance.
- Overview of generative model types: LLMs, GANs, and VAEs.
- Strengths and limitations within financial contexts.
Generative Adversarial Networks (GANs) for Finance
- Mechanism of GANs: distinguishing between generators and discriminators.
- Applications in creating synthetic data and simulating fraud scenarios.
- Case study: Generating realistic transaction data for testing purposes.
Large Language Models (LLMs) and Prompt Engineering
- Understanding how LLMs process and generate financial text.
- Developing prompts for forecasting and risk analysis.
- Practical use cases: summarizing financial reports, Know Your Customer (KYC) processes, and detecting red flags.
Financial Forecasting with Generative AI
- Time series forecasting utilizing hybrid LLM and machine learning models.
- Scenario generation and stress testing techniques.
- Use case: Predicting revenue by integrating structured and unstructured data.
Fraud Detection and Anomaly Identification
- Employing GANs for anomaly detection in transaction data.
- Spotting emerging fraud patterns through prompt-based LLM workflows.
- Evaluating model performance: balancing false positives against true risk indicators.
Regulatory and Ethical Implications
- Ensuring explainability and transparency in generative AI outputs.
- Addressing risks of model hallucination and bias in financial applications.
- Aligning with regulatory standards (e.g., GDPR, Basel guidelines).
Designing Generative AI Use Cases for Financial Institutions
- Developing business cases for internal adoption.
- Balancing innovation with risk management and compliance requirements.
- Establishing governance frameworks for the responsible deployment of AI.
Summary and Next Steps
Requirements
- A foundational understanding of finance and risk management principles.
- Experience using spreadsheets or performing basic data analysis.
- While helpful, prior familiarity with Python is not mandatory.
Target Audience
- Risk managers.
- Compliance analysts.
- Financial auditors.
14 Hours
Testimonials (1)
Trainer was very knowledgeable and easy to speak to