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Course Outline
Introduction to Generative AI
- Overview of generative models and their relevance to the finance sector.
- Types of generative models: LLMs, GANs, VAEs.
- Strengths and limitations within financial contexts.
Generative Adversarial Networks (GANs) for Finance
- How GANs function: generators versus discriminators.
- Applications in synthetic data generation and fraud simulation.
- Case study: generating realistic transaction data for testing purposes.
Large Language Models (LLMs) and Prompt Engineering
- How LLMs interpret and generate financial text.
- Designing prompts for forecasting and risk analysis.
- Use cases: financial report summarisation, KYC, and red flag detection.
Financial Forecasting with Generative AI
- Time series forecasting using hybrid LLM and machine learning models.
- Scenario generation and stress testing.
- Use case: revenue prediction leveraging both structured and unstructured data.
Fraud Detection and Anomaly Identification
- Leveraging GANs for anomaly detection in transactions.
- Identifying emerging fraud patterns through prompt-based LLM workflows.
- Model evaluation: balancing false positives against true risk indicators.
Regulatory and Ethical Implications
- Ensuring explainability and transparency in generative AI outputs.
- Risks of model hallucination and bias in financial applications.
- Compliance with regulatory expectations (e.g., GDPR, Basel guidelines).
Designing Generative AI Use Cases for Financial Institutions
- Building business cases for internal adoption.
- Balancing innovation with risk and compliance requirements.
- Governance frameworks for responsible AI deployment.
Summary and Next Steps
Requirements
- A foundational understanding of finance and risk management concepts.
- Experience with spreadsheets or basic data analysis.
- Familiarity with Python is advantageous but not essential.
Target Audience
- Risk managers
- Compliance analysts
- Financial auditors
14 Hours
Testimonials (1)
Trainer was very knowledgeable and easy to speak to