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Course Outline
AI Foundations for Finance Professionals
- Understanding AI and machine learning principles within a financial context
- Overview of AI model types: classification, regression, and generative models
- Principles of Responsible AI: focusing on accuracy, transparency, and ethical application in reporting
Automation of Financial Data Processing
- Utilizing AI tools for efficient data ingestion and extraction from PDFs and spreadsheets
- Techniques for cleaning and transforming data to prepare it for analysis
- Applying OCR, NLP, and LLMs to decode and interpret unstructured financial text
AI-Enhanced Financial Statement Analysis
- Performing automated ratio analysis and benchmarking
- Identifying trends and conducting variance analysis through machine learning
- Presenting insights through AI-powered dashboards for better visualization
Generative AI for Narrative Reporting
- Drafting executive summaries and variance commentary with the aid of LLMs
- Generating Management Discussion & Analysis (MD&A) content supported by AI
- Mastering prompt engineering to ensure accurate and compelling financial storytelling
AI-Powered Scenario Planning and Forecasting
- Foundations of scenario modeling and simulation using machine learning
- Developing dynamic models for predicting revenue, expenses, and cash flows
- Conducting stress tests on financials based on macroeconomic assumptions
Integrating AI into FP&A Workflows
- Enhancing existing spreadsheet workflows using Python or AI-based plugins
- Leveraging collaborative tools and automation to simplify monthly and quarterly close processes
- Incorporating AI capabilities into Excel, Power BI, or cloud-based FP&A platforms
Audit, Governance, and Internal Controls
- Ensuring AI explainability and readiness for internal audits
- Maintaining proper documentation of assumptions and AI outputs for compliance purposes
- Establishing robust controls for AI-assisted processes within financial reporting
Summary and Next Steps
Requirements
- Strong working knowledge of core financial statements and key metrics.
- Practical experience with spreadsheets or fundamental data analysis tools.
- Basic familiarity with Python or a readiness to work with AI-enhanced user interfaces.
Target Audience
- Corporate Finance Analysts
- FP&A Teams
- Controllers
14 Hours
Testimonials (1)
The background / theory of LLMs, the exercise
