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Finance Reconciliation Automation: Zero Human Intervention
400 hours saved per month
400+
Hours saved per month
< 4 hours
Reconciliation time
91%
Discrepancy rate reduction
3
Business units automated
The Challenge
A multi-division financial services group was running month-end reconciliation across three business units using manual spreadsheet processes, taking a team of finance analysts up to two weeks each cycle and producing frequent discrepancies.
Our Solution
We built a hybrid RPA + LLM automation pipeline. Robotic process automation handles structured data extraction from banking portals and ERP exports. A fine-tuned LLM handles unstructured transaction narratives, matching them to ledger entries using semantic similarity. Discrepancies are flagged automatically with suggested resolutions for human sign-off.
The Outcome
Month-end reconciliation now completes in under 4 hours instead of two weeks. The team saves over 400 analyst hours per month. Discrepancy rates dropped by 91%, and every action in the pipeline is fully logged for audit.
Tech Stack
PythonGPT-4oUiPath RPALangChainFastAPIPostgreSQLAWSReactDocker
Category
AI AutomationBuild something similar?
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