Automating Financial Data Integration for Tyler Incode

Challenges Faced

• The municipality received financial export data from an external system in a format that was not directly compatible with Tyler Incode.
• Manual data entry was time-consuming and prone to human errors.
• The finance team lacked an automated solution to validate, transform, and properly format transactions for seamless import.
• Missing or incorrect fields in the import process led to data discrepancies in reports, requiring additional reconciliation.

Our Approach & Process

To streamline the integration, we developed a custom Python automation script that:

1. Extracted financial data from the external system’s export files.
2. Validated the data to ensure required fields were present and properly formatted.
3. Transformed the data into a format compatible with Tyler Incode's import specifications.
4. Automated the import process, eliminating manual data entry.
5. Generated reports and error logs, providing the finance team with a clear view of any potential discrepancies before final submission.

Technologies used:

• Python & Pandas for data manipulation and transformation.
• Regex-based validation to ensure field accuracy.
• Automated file parsing and reformatting to match Tyler Incode’s import structure.

Impact & Benefits

99.9% data accuracy, reducing reconciliation efforts.

80% reduction in manual processing time, allowing the finance team to focus on higher-value tasks.

Seamless integration between the external system and Tyler Incode, ensuring faster financial reporting.

Automated validation checks, preventing incorrect data imports and reducing errors.