SPED Cross-Check Automation
Automation system built to parse SPED tax files, collect fiscal data from government portals, cross-validate multiple datasets, and generate Excel reconciliation reports for accounting workflows.
Outcome
Reduced up to ~3 hours of manual work per analyst by automating collection, validation, and reconciliation steps that were previously repetitive and error-prone.
Context
In accounting and fiscal operations, analysts often need to compare information from SPED files with external fiscal sources and manual reports. This process is repetitive, detail-heavy, and highly sensitive to inconsistencies.
Before automation, much of the workflow depended on manually collecting data, interpreting fiscal files, validating information across sources, and organizing results into spreadsheets for review.
Problem
The original workflow created three main issues:
- High manual effort for recurring validation routines
- Greater exposure to human error during reconciliation
- Slow turnaround for analysts handling multiple clients
Solution
I built an automation workflow in Python that handled the critical steps of the process end to end:
- Parsing and normalizing SPED fiscal data
- Collecting complementary fiscal information from government portals
- Cross-validating data across multiple sources
- Generating Excel reconciliation reports for operational analysis
The goal was not only speed, but also consistency: turning a repetitive validation routine into a more reliable and repeatable process.
Engineering Notes
- Python used for parsing, automation, and data processing
- Web scraping routines for public fiscal portals
- Structured transformation flow for validation and reconciliation
- Excel output designed for operational review by analysts
- Architecture focused on maintainability and practical reuse
Impact
- Reduced up to ~3 hours of manual work per analyst
- Improved consistency in reconciliation workflows
- Reduced repetitive operational effort
- Made validation outputs easier to review and share internally
Confidentiality
This project is documented as a private case study. Source code, internal rules, client information, and sensitive datasets are not included due to company policies and confidentiality requirements.