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Course Outline
AI Foundations for Financial Professionals
- Understanding AI and machine learning in a financial context
- Types of AI models: classification, regression, and generative models
- Responsible AI: ensuring accuracy, transparency, and ethical use in reporting
Automating Financial Data Processing
- Utilising AI tools for data ingestion and extraction from PDFs and spreadsheets
- Cleaning and transforming data for robust analysis
- Leveraging OCR, NLP, and LLMs to interpret unstructured financial text
AI-Driven Financial Statement Analysis
- Automated ratio analysis and benchmarking
- Trend detection and variance analysis using machine learning
- Visualising insights through AI-powered dashboards
Generative AI for Narrative Reporting
- Using LLMs to draft executive summaries and variance commentary
- Creating Management Discussion & Analysis (MD&A) with AI support
- Prompt engineering for financial storytelling and accuracy control
Scenario Planning and Forecasting with AI
- Introduction to scenario modelling and simulation with ML
- Building dynamic models for revenue, expense, and cash flow forecasting
- Stress-testing financials under various macroeconomic assumptions
Integrating AI into Existing FP&A Workflows
- Augmenting spreadsheet workflows with Python or AI plugins
- Utilising collaborative tools and automation for monthly or 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
- Familiarity with key financial statements and metrics
- Experience using spreadsheets or basic data tools
- Some exposure to Python or a willingness to use AI-enhanced interfaces
Target Audience
- Corporate finance analysts
- FP&A teams
- Controllers
14 Hours
Testimonials (1)
The background / theory of LLMs, the exercise
