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Course Outline
AI Foundations for Financial Professionals
- Understanding AI and machine learning in the financial context
- Categories of AI models: classification, regression, and generative models
- Responsible AI: ensuring accuracy, transparency, and ethical application 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
- Utilizing OCR, NLP, and LLMs to interpret unstructured financial text
AI-Driven Financial Statement Analysis
- Automated ratio analysis and benchmarking
- Identifying trends and conducting variance analysis using machine learning
- Visualizing insights through AI-powered dashboards
Generative AI for Narrative Reporting
- Drafting executive summaries and variance commentary with LLMs
- Developing management discussion & analysis (MD&A) sections with AI assistance
- Prompt engineering for financial storytelling and accuracy management
Scenario Planning and Forecasting with AI
- Overview of scenario modeling and simulation using ML
- Constructing dynamic models for revenue, expense, and cash flow projections
- Stress-testing financials under various macroeconomic assumptions
Integrating AI into Existing FP&A Workflows
- Enhancing spreadsheet workflows with Python or AI plugins
- Leveraging collaborative tools and automation for monthly/quarterly closes
- Embedding AI into Excel, Power BI, or cloud-based FP&A platforms
Audit, Governance, and Internal Controls
- AI explainability and readiness for internal audits
- Documenting assumptions and AI outputs for compliance purposes
- Establishing controls for AI-assisted processes in financial reporting
Summary and Next Steps
Requirements
- Knowledge of key financial statements and metrics
- Experience with spreadsheets or basic data tools
- Exposure to Python or a 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
