Automation Win: Reconciliation Process Before & After

Technology & Innovation
1

Wanted to share a concrete automation success story that might inspire others. :zap:

The problem: Monthly bank reconciliation across 40+ accounts was taking our team 3 days of manual matching.

The solution: Built a Python-based matching engine that:

  • Imports bank and GL data automatically
  • Applies fuzzy matching on amounts, dates, and descriptions
  • Flags exceptions for human review
  • Generates the reconciliation report

The result:

  • 3 days → 4 hours (including human review of exceptions)
  • Error rate dropped from ~2% to <0.1%
  • Team morale improved significantly (nobody liked doing recon manually)

Key learnings:

  • Start with the 80% case — don’t try to automate every edge case on day one
  • Build in good exception reporting so humans can focus on what matters
  • Version control your matching rules — they evolve as you learn from exceptions

Happy to answer questions or share more details about the technical approach. :wrench:

2

This is exactly what I want to try at Greenfield! We have a similar situation with multiple entity reconciliation. Would you be open to sharing the general architecture or pointing me toward resources for getting started? :pray:

3

We did something similar at Ridgeline with vendor invoice matching — went from 2 days to about 3 hours. The fuzzy matching was the game changer. Our accuracy improvement was similar to yours. :fire: The ROI case practically wrote itself for leadership approval.