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Course Outline
Foundations of AI for Financial 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
- Data cleaning and transformation techniques for effective analysis
- Applying OCR, NLP, and LLMs to decipher unstructured financial texts
AI-Enhanced Financial Statement Analysis
- Executing automated ratio analysis and peer benchmarking
- Detecting trends and conducting variance analysis through machine learning
- Visualizing key insights using AI-driven dashboards
Generative AI for Narrative Reporting
- Employing LLMs to draft executive summaries and explanatory variance commentary
- Creating Management Discussion & Analysis (MD&A) sections with AI assistance
- Mastering prompt engineering for precise financial storytelling and accuracy control
AI-Powered Scenario Planning and Forecasting
- Introduction to scenario modeling and simulation using ML
- Constructing dynamic models for forecasting revenue, expenses, and cash flows
- Conducting stress tests on financial data under various macroeconomic conditions
Integration of AI into Existing FP&A Workflows
- Enhancing spreadsheet workflows with Python or specialized AI plugins
- Implementing 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 preparedness for internal audits
- Documenting assumptions and AI outputs to meet compliance standards
- Establishing robust controls for AI-assisted processes in financial reporting
Course Summary and Recommended Next Steps
Requirements
- Proficiency with essential financial statements and key performance metrics
- Practical experience with spreadsheets or fundamental data analysis tools
- Basic familiarity with Python or a readiness to utilize AI-enhanced user interfaces
Target Audience
- Corporate finance analysts
- Finance, Planning & Analysis (FP&A) teams
- Controllers
14 Hours
Testimonials (1)
The background / theory of LLMs, the exercise
