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Course Outline
AI Foundations for Financial Professionals
- The role of AI and machine learning in finance
- AI model types: classification, regression, and generative models
- Responsible AI: ensuring accuracy, transparency, and ethical use in reporting
Automating Financial Data Processing
- Employing AI tools for data ingestion and extraction from PDFs and spreadsheets
- Data cleaning and transformation for analytical purposes
- Interpreting unstructured financial text using OCR, NLP, and LLMs
AI-Driven Financial Statement Analysis
- Automated ratio analysis and benchmarking
- Trend detection and variance analysis via machine learning
- Visualizing insights through AI-powered dashboards
Generative AI for Narrative Reporting
- Drafting executive summaries and variance commentary with LLMs
- Assisting in the creation of management discussion & analysis (MD&A) using AI
- Prompt engineering for accurate financial storytelling
Scenario Planning and Forecasting with AI
- Introduction to scenario modeling and simulation using ML
- Developing dynamic models for forecasting revenue, expenses, and cash flow
- Stress testing financials based on macroeconomic assumptions
Integrating AI into Existing FP&A Workflows
- Enhancing spreadsheet workflows with Python or AI plugins
- Collaborative tools and automation for monthly/quarterly close processes
- Embedding AI into Excel, Power BI, or cloud-based FP&A platforms
Audit, Governance, and Internal Controls
- AI explainability and preparing for internal audits
- Documenting assumptions and AI outputs for compliance
- Establishing controls for AI-assisted financial reporting processes
Summary and Next Steps
Requirements
- Knowledge of key financial statements and metrics
- Proficiency with spreadsheets or basic data tools
- Basic Python exposure or willingness to utilize AI-enhanced interfaces
Target Audience
- Corporate finance analysts
- FP&A teams
- Controllers
14 Hours
Testimonials (1)
The background / theory of LLMs, the exercise
