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Course Outline

AI Foundations for Financial Professionals

  • Understanding AI and machine learning in the context of finance.
  • Types of AI models: classification, regression, and generative models.
  • Responsible AI: focusing on accuracy, transparency, and ethical use in reporting.

Automating Financial Data Processing

  • Using AI tools for data ingestion and extraction from PDFs and spreadsheets.
  • Cleaning and transforming data for 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.
  • Visualizing insights using 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 modeling and simulation with ML.
  • Building dynamic models for revenue, expense, and cash flow forecasting.
  • Stress testing financials under macroeconomic assumptions.

Integrating AI into Existing FP&A Workflows

  • Augmenting spreadsheet workflows with Python or AI plugins.
  • 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 internal audit readiness.
  • Documenting assumptions and AI outputs for compliance.
  • Setting 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

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