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