Write Python scripts using pandas to read, clean and consolidate data from multiple spreadsheet sources.
Finance & Banking
Automating Manual Finance Processes with Python Scripts
Build Python scripts that automate repetitive finance tasks such as reconciliation, data consolidation and reporting, with testing and controls for production use.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Finance teams still lose hours each month to manual tasks such as consolidating spreadsheets from different business units, matching transactions across systems and reformatting data for reporting, and much of this work follows a repeatable logic that a short Python script can execute in seconds once built correctly. This course teaches finance professionals with no prior programming background to write Python scripts for the tasks that consume the most manual time: reading and combining data from multiple Excel and CSV sources with pandas, building reconciliation logic that flags mismatches between two datasets, and generating formatted reports ready for review. Participants also learn the habits that separate a reliable finance automation script from a fragile one, including input validation that catches malformed data before it causes silent errors, logging that creates an audit trail of what a script did, and version control so changes to a script can be tracked and rolled back. The course covers scheduling scripts to run automatically and the review controls needed before an automated output is trusted for external reporting, so participants leave with working scripts for their own processes and the skills to build the next one.
Expected Learning Outcomes
Build reconciliation logic that flags mismatches and exceptions between two financial datasets.
Generate formatted reports and summary tables directly from a Python script for finance review.
Apply input validation techniques that catch malformed or missing data before processing continues.
Implement logging that creates an audit trail of every automated script run and its results.
Use version control to track changes to finance automation scripts over time.
Schedule scripts to run automatically and design review controls before results are used externally.
Who Should Attend
Finance analysts and accountants spending significant time on manual spreadsheet tasks.
FP&A professionals consolidating data from multiple business units or systems.
Financial controllers seeking to reduce manual effort in the month-end close process.
Finance transformation staff building an internal automation capability.
Internal audit and controls staff assessing automation scripts before production use.
Shared service centre teams processing high volumes of repetitive finance transactions.
Course Modules
Select any module to see its sessions and points.
01Python Foundations for Finance Data
2 sessions · 8 points
Session 1Reading and Cleaning Financial Data
- Read Excel and CSV files into pandas dataframes and inspect their structure for inconsistencies.
- Clean column names, data types and date formats that commonly differ across finance source files.
- Handle missing values and duplicate rows using pandas functions suited to financial datasets.
- Filter and subset transaction data based on account, date range or business unit criteria.
Session 2Consolidating Multiple Data Sources
- Combine data from several business unit spreadsheets into a single consolidated dataframe.
- Merge datasets on common keys such as account code, cost centre or transaction reference.
- Aggregate consolidated data into summary tables by period, entity or category.
- Export consolidated results to a formatted Excel workbook ready for review.
02Building Reconciliation Logic
2 sessions · 8 points
Session 1Matching Transactions Between Two Datasets
- Write matching logic comparing transaction amounts, dates and references across two datasets.
- Flag exceptions where amounts or dates fall outside an agreed tolerance for automatic matching.
- Build a summary report separating matched, unmatched and exception items for reviewer attention.
- Handle common reconciliation edge cases such as split payments and timing differences.
Session 2Automating Recurring Reconciliations
- Parameterise a reconciliation script so it can run against different accounts without code changes.
- Build a reusable function library for reconciliation logic applied across multiple processes.
- Design output formatting that highlights exceptions clearly for a non-technical reviewer.
- Test the reconciliation script against historical data to confirm it replicates manual results.
03Reliability, Validation and Audit Trail
2 sessions · 8 points
Session 1Input Validation and Error Handling
- Build validation checks that reject or flag malformed input files before processing begins.
- Write error handling that produces a clear message instead of a silent or cryptic failure.
- Test scripts against deliberately corrupted or incomplete sample data to confirm robustness.
- Document expected input file formats so process owners provide compatible data consistently.
Session 2Logging, Version Control and Audit Readiness
- Implement logging that records each script run, its inputs and its summary results.
- Use a version control system to track script changes and enable rollback if a change causes issues.
- Build a change log documenting why and when a script's logic was modified.
- Prepare documentation that would allow an auditor to understand and re-perform the automated process.
04Scheduling and Production Deployment
2 sessions · 8 points
Session 1Scheduling and Running Scripts Automatically
- Set up scheduled tasks that run finance automation scripts at defined times without manual intervention.
- Configure email or dashboard notifications summarising each automated run's outcome.
- Design a fallback manual process for use if a scheduled script fails to run correctly.
- Monitor scheduled script performance and processing time as data volumes grow.
Session 2Review Controls and Handover to Production
- Define sign-off checkpoints confirming automated output is reviewed before use in external reporting.
- Build a handover pack enabling another team member to maintain the script after the original author leaves.
- Assess segregation of duties between script development, testing and production approval.
- Present a business case quantifying time saved and error reduction from a completed automation project.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
Complete your registration
We will contact you within one business day to confirm.
Ready to start?
Reserve your seat and start building the skill.