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Course Outline
Foundations of Generative AI
- An overview of generative models and their significance within the financial industry.
- Classification of generative models, including LLMs, GANs, and VAEs.
- Analysis of strengths and constraints when applied to financial contexts.
Leveraging Generative Adversarial Networks (GANs) in Finance
- Mechanisms of GANs: the interplay between generators and discriminators.
- Practical applications in synthetic data creation and fraud simulation.
- Case study: Producing realistic transaction datasets for testing purposes.
Large Language Models (LLMs) and Prompt Engineering Strategies
- Understanding how LLMs process and generate financial documentation.
- Crafting effective prompts for forecasting and risk assessment.
- Key applications: Summarizing financial reports, Know Your Customer (KYC) processes, and identifying red flags.
Advanced Financial Forecasting with Generative AI
- Time series prediction utilizing hybrid LLM and machine learning models.
- Generating scenarios and conducting stress tests.
- Application: Predicting revenue by integrating both structured and unstructured data sources.
Enhancing Fraud Detection and Anomaly Identification
- Employing GANs to detect anomalies in transaction patterns.
- Recognizing emerging fraud trends through LLM workflows driven by prompts.
- Model assessment: Distinguishing between false positives and genuine risk indicators.
Regulatory and Ethical Dimensions
- Ensuring explainability and transparency in generative AI outputs.
- Addressing risks related to model hallucinations and bias within financial operations.
- Adhering to regulatory standards, such as GDPR and Basel guidelines.
Structuring Generative AI Use Cases for Financial Institutions
- Developing business cases for internal adoption.
- Striking a balance between innovation and risk management or compliance.
- Establishing governance frameworks for responsible AI implementation.
Conclusion and Future Directions
Requirements
- A solid grounding in core finance and risk management principles.
- Practical experience with spreadsheet tools or fundamental data analysis techniques.
- Knowledge of Python is advantageous but is not a mandatory requirement.
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