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Course Outline
AI Foundations for Financial Professionals
- Understanding AI and machine learning within the financial context
- Types of AI models: classification, regression, and generative models
- Responsible AI: ensuring accuracy, transparency, and ethical usage in reporting
Automating Financial Data Processing
- Utilising AI tools for data ingestion and extraction from PDFs and spreadsheets
- Data cleaning and transformation for analysis
- Leveraging OCR, NLP, and LLMs to interpret unstructured financial text
AI-Driven Financial Statement Analysis
- Automated ratio analysis and benchmarking
- Detecting trends and analysing variances using machine learning
- Visualising insights via AI-powered dashboards
Generative AI for Narrative Reporting
- Drafting executive summaries and variance commentary using LLMs
- Creating management discussion & analysis (MD&A) with AI support
- Applying prompt engineering for financial storytelling and accuracy control
Scenario Planning and Forecasting with AI
- Introduction to scenario modeling and simulation using ML
- Building dynamic models for forecasting revenue, expenses, and cash flow
- Stress testing financials under macroeconomic assumptions
Integrating AI into Existing FP&A Workflows
- Enhancing spreadsheet workflows with Python or AI plugins
- Using collaborative tools and automation for monthly/quarterly closes
- Embedding AI into Excel, Power BI, or cloud FP&A platforms
Audit, Governance, and Internal Controls
- AI explainability and readiness for internal audit
- Documenting assumptions and AI outputs for compliance
- Establishing controls for AI-assisted processes in financial reporting
Summary and Next Steps
Requirements
- Familiarity with key financial statements and metrics.
- Experience with spreadsheets or basic data tools.
- Some exposure to Python or a willingness to use AI-enhanced interfaces.
Audience
- Corporate finance analysts.
- FP&A teams.
- Controllers.
14 Hours
Testimonials (1)
The background / theory of LLMs, the exercise
