To provide CPAs and other financial professionals with the latest cutting-edge tools and techniques to develop effective, efficient and robust models. You will learn how to use the models you create to produce actionable forecasts and business analyses.

• Overview of Financial Budgeting 
  – Key components
  – Budgeting methods and KPIs
  – Best practices
• Corporate Budgeting
• Forecasting and Modeling Tools—Excel and Other Software Options
• Financial Forecasting
  – Objectives
  – Types of financial forecasting
• Evaluating Budgeting and Forecasting Methods
• Project Forecasting
• Common Forecasting Pitfalls
• Financial Modeling
  – Objectives
  – Key components
  – Types of models
  – Key metrics and KPIs
  – Example model structure
• Financial Analysis
• Ratio Analysis
• Valuation Ratios
• Financial Variance Analysis
• AI in Financial Forecasting
  – Core components of an AI forecasting system
  – Key forecastable metrics
  – Forecasting model types and use cases
  – Real-world applications
  – Governance and controls
  – Best practices

To provide CPAs and other financial professionals with the latest cutting-edge tools and techniques to develop effective, efficient and robust models. You will learn how to use the models you create to produce actionable forecasts and business analyses.

DETAILED LEARNING OBJECTIVES

• Identify the primary purpose of financial budgeting

• Recognize the key components of a financial budget

• Explain the role of capital expenditures (CapEx) in budgeting

• Describe how budgeting supports strategic decision-making

• Distinguish between different budgeting methods, including zero-based and incremental

• Understand the function and structure of a rolling budget

• Differentiate between top-down and bottom-up budgeting approaches

• Calculate and interpret budget utilization and budget variance KPIs

• Apply best practices in aligning budgets with organizational goals

• Describe the purpose and components of a master budget

• Identify the types and uses of corporate budget categories

• Explain how flexible budgets adjust to changes in activity levels

• Outline the steps of the corporate budgeting process

• Understand how ROI is used to evaluate capital effectiveness

• Recognize common challenges in corporate budgeting and how to address them

• Apply rolling forecasts as a budgeting best practice

• Match appropriate tools (e.g., Adaptive Planning, Power BI) to forecasting needs

• Compare tools used by small businesses and enterprise organizations

• Describe the purpose of financial forecasting in strategic planning

• Identify types of financial forecasts such as revenue, expense, and cash flow

• Match forecasting methods (e.g., historical averaging, regression) to appropriate use cases

• Use trend and moving average techniques to forecast short-term performance

• Explain how regression analysis identifies relationships between financial variables

• Understand the logic and application of driver-based forecasting

• Develop scenario plans to manage risk under uncertainty

• Recognize how AI and machine learning enhance forecasting accuracy

• Evaluate the strengths of LSTM and other AI models for time-series forecasting

• Identify the limitations and implementation challenges of AI forecasting

• Interpret forecast accuracy metrics such as MAPE and forecast bias

• Describe best practices for integrating AI forecasting into business decision-making 

• Overview of Financial Budgeting 
  – Key components
  – Budgeting methods and KPIs
  – Best practices
• Corporate Budgeting
• Forecasting and Modeling Tools—Excel and Other Software Options
• Financial Forecasting
  – Objectives
  – Types of financial forecasting
• Evaluating Budgeting and Forecasting Methods
• Project Forecasting
• Common Forecasting Pitfalls
• Financial Modeling
  – Objectives
  – Key components
  – Types of models
  – Key metrics and KPIs
  – Example model structure
• Financial Analysis
• Ratio Analysis
• Valuation Ratios
• Financial Variance Analysis
• AI in Financial Forecasting
  – Core components of an AI forecasting system
  – Key forecastable metrics
  – Forecasting model types and use cases
  – Real-world applications
  – Governance and controls
  – Best practices