AI in Budgeting & Rolling Forecasts

Finance teams are increasingly replacing rigid annual budgets with AI-enhanced rolling forecasts to adapt dynamically to market volatility. This course combines advanced ML forecasting, generative scenario planning, and automation to build continuous budget models. Delegates will leverage cutting-edge tools Prophet, AutoML, LLMs, and cloud platforms—to create driver-based forecasts that are agile, accurate, and executive-ready.

TRAINING TOPICS

• The shift from static budgets to rolling forecasts
• Time-series forecasting: ARIMA, Prophet, LSTM for financial planning
• Driver-based budgeting: integrating internal and external KPIs
• Generative AI for scenario generation and narrative budgeting
• ML-powered anomaly detection and accuracy flags
• Automating budget pipelines with AutoML & RPA
• Explainable AI & auditability with SHAP/LIME
• BI-driven visualization in Power BI/Tableau with live driver insights
• Nowcasting for in-period adjustments
• Governance and sustainability analysis in financial planning

BY THE END OF THIS TRAINING COURSE, DELEGATES WILL BE ABLE TO

• Transition from static to rolling budget models
• Build and validate time-series forecasts using ML techniques
• Develop driver-based budgeting frameworks using AI
• Generate executive forecast narratives with LLMs
• Automate forecasting pipelines with AutoML and RPA
• Detect anomalies and adjust budgets proactively
• Visualize forecasts with BI dashboards and explainable narratives
• Govern forecasting processes with audit logs and ESG tracking

TRAINING IS TAILORED TO

• FP&A professionals and budgeting teams
• Financial controllers and treasury analysts
• Business operations & demand planners
• Data scientists working on finance use cases
• BI developers supporting finance functions
• Finance transformation and ERP integration leads

TRAINING METHODOLOGY

• Instructor-led demos of ML budgeting models
• Hands-on labs using Prophet, LSTM, AutoML
• Generative AI for scenario and narrative creation
• Pipeline automation using cloud tools & RPA
• Explainability implementation using SHAP/LIME
• Dashboard creation using Power BI or Tableau
• Peer reviews and governance checklist development

Day One:

FROM STATIC BUDGETS TO AI‑ENABLED ROLLING FORECASTS

• Concept overview: why rolling forecasts matter
• Financial data prep and time-series structure
• Lab: ARIMA and Prophet forecasting models
• Case study: real-time variance management

Day Two:

DRIVER‑BASED BUDGETING WITH AI

• Identifying and modeling key budget drivers
• Feature engineering for financial planning
• Lab: multivariate forecasting with exogenous variables
• Scenario: mapping drivers to budget outcomes

Day Three:

GENERATIVE SCENARIOS & FORECAST NARRATIVES

• Using LLMs for narrative budgeting
• Lab: generate quarterly forecast commentary
• Integration: LLMs + BI dashboards
• Peer review of narrative clarity

Day Four:

AUTOMATION, ANOMALY DETECTION & NOWCASTING

• Pipeline automation with AutoML & RPA
• Anomaly detection in forecast series
• Lab: end-to-end automated forecast flow
• Use case: nowcasting for mid-cycle budget updates

Day Five:

EXPLAINABILITY, ESG MONITORING & GOVERNANCE

• Explainable AI techniques for budgeting models
• Lab: apply SHAP to rolling forecasts
• Integrate ESG / sustainability indicators in budgets
• Group project: full budget governance framework