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Course Outline
Introduction to Generative AI
- Exploring the relevance of generative models to the financial sector
- Types of generative models: LLMs, GANs, VAEs
- Strengths and limitations in financial contexts
Generative Adversarial Networks (GANs) for Finance
- How GANs work: generators vs discriminators
- Applications in synthetic data generation and fraud simulation
- Case study: generating realistic transaction data for testing
Large Language Models (LLMs) and Prompt Engineering
- How LLMs understand and generate financial text
- Designing prompts for forecasting and risk analysis
- Use cases: financial report summarization, KYC, red flag detection
Financial Forecasting with Generative AI
- Time series forecasting with hybrid LLM and ML models
- Scenario generation and stress testing
- Use case: revenue prediction using structured and unstructured data
Fraud Detection and Anomaly Identification
- Using GANs for anomaly detection in transactions
- Identifying emerging fraud patterns through prompt-based LLM workflows
- Model evaluation: false positives vs true risk indicators
Regulatory and Ethical Implications
- Explainability and transparency in generative AI outputs
- Risk of model hallucination and bias in finance
- 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
- Governance frameworks for responsible AI deployment
Summary and Next Steps
Requirements
- A solid grasp of fundamental finance and risk management principles
- Practical experience with spreadsheets or basic data analysis techniques
- Prior knowledge of Python is advantageous but not mandatory
Target Audience
- Risk managers
- Compliance analysts
- Financial auditors
14 Hours
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
