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Course Outline
Foundations of AI for Financial Professionals
- Understanding AI and machine learning within the financial context.
- Overview of AI model types: classification, regression, and generative models.
- Responsible AI practices: ensuring accuracy, transparency, and ethical application in reporting.
Automating Financial Data Processing
- Utilizing AI tools to ingest and extract data from PDFs and spreadsheets.
- Techniques for cleaning and transforming data to prepare it for analysis.
- Applying OCR, NLP, and Large Language Models (LLMs) to interpret unstructured financial texts.
AI-Powered Financial Statement Analysis
- Performing automated ratio analysis and industry benchmarking.
- Detecting trends and conducting variance analysis through machine learning.
- Visualizing key insights via AI-driven dashboards.
Generative AI for Narrative Reporting
- Drafting executive summaries and variance commentary using LLMs.
- AI-assisted creation of Management Discussion & Analysis (MD&A) sections.
- Prompt engineering strategies to ensure financial storytelling accuracy and control.
Scenario Planning and Forecasting with AI
- Introductory scenario modeling and simulation using machine learning.
- Developing dynamic models for forecasting revenue, expenses, and cash flows.
- Conducting stress tests on financials based on macroeconomic assumptions.
Integrating AI into Existing FP&A Workflows
- Enhancing spreadsheet workflows with Python or AI plugins.
- Implementing collaborative tools and automation for monthly or quarterly close processes.
- 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 within financial reporting.
Summary and Path Forward
Requirements
- A solid understanding of key financial statements and core metrics.
- Practical experience with spreadsheets or basic data handling tools.
- Exposure to Python or a readiness to utilize AI-enhanced interfaces.
Target Audience
- Corporate finance analysts.
- Financial Planning and Analysis (FP&A) teams.
- Controllers.
14 Hours
Testimonials (1)
The background / theory of LLMs, the exercise
