Data and Analytics

A payment reconciliation agent cut manual effort by 65%

An AI agent automates payment reconciliation end to end, matching payments to invoices with explainable, audit ready reasoning, built for a leading European self storage operator.

The Opportunity

Finance teams had no way to see or understand how payments were being matched, and a significant share of staff time went to reviewing and resolving unmatched payments by hand.

A recurring tail of unreconciled wire transfers created cash flow uncertainty and delayed the financial close process every period, and finance teams had no ability to tune or extend the matching logic as the business evolved.

The Transformation

A payment reconciliation agent extracts and normalizes bank statements and invoices into a match ready format, applies business rules first, then uses vector search and LLM reasoning to match payments to invoices.

Matches above 90% confidence are approved and exported automatically, and everything else surfaces in a structured exception queue with a plain language explanation for every decision.

  1. Payment & Invoice Ingestion

    Bank statements and invoices are extracted and normalized into a unified, match ready format automatically, removing the manual preparation that used to come before matching could even start.

  2. Rules First Matching Engine

    Deterministic rules clear the clean, unambiguous matches up front, so the heavier vector search and LLM reasoning only run on what the rules can't settle, each layer doing the part it's best at.

  3. Confidence Based Automatic Approval

    Only the matches the agent is 90%+ confident in clear on their own, posting straight through without review, so staff shift from checking every payment to resolving just the ones held back.

  4. Explainable Decisions & Audit Trail

    Every match, automatic or flagged, comes with a plain language explanation, and unmatched payments surface in a structured queue with user, timestamp, and reasoning logged for every decision.

Impact
65%

Reduction in manual reconciliation effort

90%

Automated matching rate the agent is designed to reach

~20–25%

Faster financial close cycle time

100%

Audit trail, every match decision logged with user, timestamp, and reasoning

Tech Stack: LangGraph, Azure App Service, Azure SQL, Mistral 7B
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