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Duration 14 hours
Course Outline
Foundations of Generative AI
- An overview of generative models and their strategic importance in finance
- Classification of generative models: LLMs, GANs, and VAEs
- Strengths and inherent limitations when applied to financial contexts
Utilizing Generative Adversarial Networks (GANs) in Finance
- Understanding GAN mechanics: the interplay between generators and discriminators
- Practical applications in synthetic data creation and fraud scenario simulation
- Case study: producing realistic transaction data for robust testing
Large Language Models (LLMs) and Advanced Prompt Engineering
- How LLMs interpret and produce financial text
- Crafting effective prompts for forecasting and risk assessment
- Real-world use cases: summarizing financial reports, KYC processes, and red flag identification
Enhancing Financial Forecasting with Generative AI
- Time series forecasting using hybrid approaches combining LLMs and machine learning
- Generating scenarios and conducting stress tests
- Practical example: predicting revenue by integrating structured and unstructured data sources
Fraud Detection and Anomaly Recognition
- Employing GANs to detect anomalies within transaction streams
- Identifying emerging fraud patterns via prompt-driven LLM workflows
- Evaluating model performance: 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
- Adhering to regulatory standards (e.g., GDPR, Basel guidelines)
Strategizing Generative AI Applications for Financial Institutions
- Developing compelling business cases for internal adoption
- Striking a balance between innovation and risk/compliance obligations
- Implementing governance frameworks for responsible AI deployment
Recap and Future Directions
Requirements
- Solid grounding in fundamental finance and risk management principles
- Proficiency in using spreadsheets or basic data analysis tools
- Knowledge of Python is advantageous but 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
