Programme overview
Technical depth: Practitioner · Practical mode: Lab
Introduction
Finance teams still spend much of each close copying ledger extracts, repairing spreadsheets and rebuilding the same reports by hand, which leaves little time for analysis and introduces errors that reach management packs. This Core Concept course shows finance professionals how to automate that work with Power Query, Power BI and Copilot while keeping the controls auditors expect. Every session is hands-on in the software, using multi-entity finance data, and each participant leaves with an Automated Month-End Reporting Pack built around one of their own finance processes.
Course Objectives
- Identify and score finance tasks that are suitable for automation, generative AI or no change
- Build repeatable Power Query transformations that consolidate and cleanse ledger, bank and budget data
- Design a Power BI star-schema model with DAX measures for actual, budget and prior-period reporting
- Use Copilot in Excel and Power BI to draft formulas, queries and variance commentary, and verify the output before use
- Apply spreadsheet and automation controls, including access, change logging and review evidence, to automated reports
- Produce an Automated Month-End Reporting Pack for an own finance process, with a control matrix
Target Audience
- Accountants responsible for month-end close, journals and account reconciliations
- Financial and management reporting analysts who prepare monthly packs and dashboards
- FP&A analysts responsible for budgets, forecasts and variance analysis
- Treasury and cash management officers who reconcile bank data and forecast cash
- Finance systems and reporting officers who maintain Excel models and Power BI reports
Course Outline
Day 1: Finance Automation Landscape and Current State
- Automation Opportunity Mapping Across Record-to-Report and Procure-to-Pay
- Current-State Close Assessment Using a Close Calendar and Task Time Log
- Rules-Based Automation, Machine Learning and Generative AI Compared for Finance Tasks
- Microsoft 365 Copilot Data Boundaries and Sensitivity Labels for Financial Data
- Automation Candidate Scoring Matrix: Volume, Rules, Risk and Effort
Day 2: Data Architecture and Control Frameworks
- Power Query M Transformation Steps and Query Folding
- Star Schema Design for General Ledger, Budget and Actuals
- ISO/IEC 25012 Data Quality Characteristics Applied to Ledger Extracts
- DAX Measures and Time Intelligence for Month, Quarter and Year-to-Date Reporting
- COSO Internal Control Framework Applied to End-User Computing and Automated Workflows
Day 3: Building Automated Finance Reports
- Power Query Lab: Consolidating Multi-Entity Trial Balances from a Folder
- Bank Reconciliation Automation with Power Query Merges and Fuzzy Matching
- Power BI Variance Dashboard Build Using IBCS Notation
- Copilot in Excel for Formula Drafting, Data Cleansing and Pivot Analysis
- Copilot in Power BI for DAX Query Drafting and Report Page Summaries
Day 4: Forecasting, Workflow and Control Risks
- Excel Forecast Sheet and Exponential Smoothing (ETS) for Cash Flow Forecasting
- Power Automate Approval Flows for Journal and Report Sign-Off
- AI Output Reviewer Checklist for Calculation Errors and Hallucinated Figures
- Row-Level Security and Workspace Access Design in Power BI
- Automation Change Log, Version Control and Audit Trail Evidence
Day 5: Month-End Lab and the Automated Reporting Pack
- Month-End Close Lab: End-to-End Refresh from Ledger Extract to Management Pack
- Variance Commentary Drafting with Copilot and Human Review
- Automated Month-End Reporting Pack Build for an Own Finance Process
- Control Matrix and Handover Documentation for the Automated Pack
- Peer Demonstration and Time-Saving Estimate
Skills You Will Gain
- Finance Process Automation
- Power Query Data Transformation
- Dimensional Data Modelling
- DAX Measure Writing
- AI-Assisted Financial Analysis
- Financial Dashboard Design
- End-User Computing Controls
- Cash Flow Forecasting
Why Attend This Course
- Return to work with an Automated Month-End Reporting Pack that already runs on data shaped like your own
- Cut the manual rework in reconciliations and reporting that delays each close
- Use Copilot with confidence because you know how to check what it produces before it reaches a report
- Exchange automation techniques with finance professionals from other sectors and countries
Conclusion
Automation only helps finance when the numbers it produces can be trusted and explained. This course moves from mapping which tasks to automate, through the data model, Power Query, DAX and Copilot skills that do the work, to the forecasting, approval and control issues that decide whether an automated report survives audit. The final day brings those skills together in an Automated Month-End Reporting Pack that participants take back to their team, ready to shorten the next close.