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Duration 14 hours
Course Outline
Introduction to Generative AI
- Exploring generative models and their specific value to the finance industry
- Classifying generative architectures: LLMs, GANs, and VAEs
- Analyzing the strengths and constraints of these models in financial settings
Applying Generative Adversarial Networks (GANs) to Finance
- Demystifying GAN mechanics: the interplay between generators and discriminators
- Leveraging GANs for synthetic data creation and simulating fraud scenarios
- Practical application: Producing realistic transaction data for rigorous testing
Large Language Models (LLMs) and the Art of Prompt Engineering
- Understanding how LLMs process and generate financial narratives
- Crafting effective prompts for predictive forecasting and risk assessment
- Real-world uses: Summarizing financial reports, KYC verification, and spotting red flags
Advancing Financial Forecasting with Generative AI
- Implementing time series forecasting through hybrid LLM and ML frameworks
- Generating complex scenarios and conducting stress tests
- Case study: Predicting revenue by integrating structured and unstructured data sources
Enhancing Fraud Detection and Anomaly Identification
- Utilizing GANs to uncover anomalies within transaction streams
- Detecting emerging fraud trends via prompt-driven LLM workflows
- Evaluating model performance: Distinguishing false positives from genuine risk signals
Navigating Regulatory and Ethical Landscapes
- Ensuring explainability and transparency in AI-generated outputs
- Mitigating risks associated with model hallucination and algorithmic bias
- Aligning with regulatory standards, including GDPR and Basel guidelines
Developing Generative AI Strategies for Financial Institutions
- Constructing compelling business cases for internal AI adoption
- Striking a balance between technological innovation and risk/compliance obligations
- Establishing governance frameworks for responsible AI implementation
Recap and Future Pathways
Requirements
- A solid grasp of fundamental finance and risk management principles
- Proficiency with spreadsheets or basic data analysis tools
- Working knowledge of Python is advantageous, though not mandatory
Target Audience
- Risk management professionals
- Compliance specialists
- Financial auditors
Testimonials (1)
i already have some reports that i know, i will use some of the prompts that looked at today
