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Course Outline
Introduction to Generative AI
- Exploring the significance of generative models within the financial landscape.
- Distinguishing between various model types, including LLMs, GANs, and VAEs.
- Analyzing the strengths and constraints of these models in financial applications.
Leveraging Generative Adversarial Networks (GANs) in Finance
- Understanding the GAN mechanism: the interplay between generators and discriminators.
- Applying GANs for synthetic data creation and fraud scenario simulation.
- Case study: Producing realistic transaction datasets for testing purposes.
Large Language Models (LLMs) and Advanced Prompt Engineering
- Understanding how LLMs process and generate financial documentation.
- Constructing strategic prompts for forecasting and risk assessment.
- Practical applications: Summarizing financial reports, KYC processes, and identifying red flags.
Enhancing Financial Forecasting with Generative AI
- Implementing time series forecasting through hybrid LLM and ML architectures.
- Generating scenarios and conducting stress tests.
- Use case: Forecasting revenue by integrating structured and unstructured data sources.
Advancing Fraud Detection and Anomaly Identification
- Utilizing GANs to detect anomalies within transactional data.
- Detecting emerging fraud trends using prompt-driven LLM workflows.
- Assessing model performance: differentiating between false positives and genuine risk signals.
Navigating Regulatory and Ethical Challenges
- Ensuring explainability and transparency in the outputs of generative AI systems.
- Addressing risks related to model hallucinations and biases in financial contexts.
- Adhering to regulatory standards, such as GDPR and Basel guidelines.
Strategic Deployment of Generative AI in Financial Institutions
- Formulating business cases for internal adoption of AI technologies.
- Striking a balance between innovation and adherence to risk and compliance protocols.
- Establishing governance frameworks for the responsible deployment of AI.
Conclusion and Path Forward
Requirements
- A foundational understanding of finance and risk management principles.
- Proficiency with spreadsheets or basic data analysis tools.
- 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
