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Duration 14 hours
Course Outline
Foundations of Generative AI
- A broad perspective on generative models and their significance to the financial industry
- Classification of generative models, including LLMs, GANs, and VAEs
- Analyzing the strengths and constraints of these models in financial settings
Leveraging 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
Large Language Models (LLMs) and Prompt Engineering
- How LLMs process and produce financial texts
- Formulating prompts tailored for forecasting and risk assessment
- Key applications: summarizing financial reports, KYC processes, and identifying red flags
Financial Forecasting via Generative AI
- Time series prediction using hybrid LLM and Machine Learning models
- Generating scenarios for stress testing
- Application: forecasting revenue by integrating structured and unstructured data
Fraud Detection and Anomaly Recognition
- Employing GANs to detect anomalies in transaction patterns
- Uncovering emerging fraud trends through LLM-based prompt workflows
- Model assessment: distinguishing false positives from genuine risk indicators
Regulatory and Ethical Considerations
- Ensuring explainability and transparency in generative AI outputs
- Mitigating risks associated with model hallucinations and bias in finance
- Aligning with regulatory standards (e.g., GDPR, Basel guidelines)
Developing Generative AI Use Cases for Financial Institutions
- Constructing business cases to drive internal adoption
- Striking a balance between innovation and risk/compliance requirements
- Establishing governance frameworks for responsible AI deployment
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental finance and risk management principles
- Practical experience with spreadsheets or foundational data analysis tools
- Knowledge of Python is advantageous, though not mandatory
Target Audience
- Risk managers
- Compliance analysts
- Financial auditors
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
