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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

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