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Course Outline
Foundations of AI for Finance Professionals
- Defining AI and machine learning within the financial sector.
- Overview of AI model types: classification, regression, and generative models.
- Responsible AI practices: ensuring accuracy, transparency, and ethical application in reporting.
Automation of Financial Data Processing
- Implementing AI tools for data ingestion and extraction from PDFs and spreadsheets.
- Preparing data through cleaning and transformation for rigorous analysis.
- Applying OCR, NLP, and LLMs to decode unstructured financial text.
AI-Powered Financial Statement Analysis
- Executing automated ratio analysis and industry benchmarking.
- Utilizing machine learning for trend identification and variance analysis.
- Visualizing key insights through AI-driven dashboards.
Generative AI for Narrative Reporting
- Drafting executive summaries and variance commentary using LLMs.
- Developing management discussion & analysis (MD&A) sections with AI assistance.
- Mastering prompt engineering for financial storytelling while maintaining accuracy.
AI-Enhanced Scenario Planning and Forecasting
- Introduction to scenario modeling and simulation using machine learning.
- Constructing dynamic models for forecasting revenue, expenses, and cash flows.
- Conducting stress tests on financials based on macroeconomic assumptions.
Integration of AI into Existing FP&A Workflows
- Enhancing spreadsheet workflows through Python or specialized AI plugins.
- Utilizing collaborative tools and automation for monthly and quarterly closes.
- Embedding 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.
- Documenting assumptions and AI outputs to meet compliance standards.
- Establishing controls for AI-assisted processes in financial reporting.
Summary and Future Steps
Requirements
- A solid understanding of core financial statements and key performance metrics.
- Practical experience with spreadsheets or fundamental data management tools.
- Prior exposure to Python or a readiness to work with AI-enhanced interfaces.
Target Audience
- Corporate finance analysts.
- FP&A teams.
- Controllers.
14 Hours
Testimonials (1)
The background / theory of LLMs, the exercise
