Every growing finance team eventually hits the same wall. The recs backlog grows faster than headcount. You hire two more analysts. Six months later, the backlog is the same size and the analysts are doing the same work the last two did.
The problem isn't capacity. It's that you're paying qualified accountants to do matching, and matching is the part of the job a machine should have taken over a decade ago.
What an agent actually does
A balance sheet reconciliation agent is not a fancy macro. It does three things:
- Reconciles the easy ones. Bank, intercompany, suspense, payroll clearing — anything where the match logic is rules-based with a sensible tolerance. These should never touch a human.
- Investigates the awkward ones. Pulls the underlying transactions, cross-references the sub-ledger, drafts a one-line explanation, and presents the analyst with a working position instead of a blank screen.
- Escalates the genuine exceptions. With context, with a proposed treatment, and with a clear note on why the agent stopped.
The maths is unglamorous. In most teams we've measured, 70-85% of reconciling items are recurring patterns the agent can fully resolve, another 10-15% are awkward but tractable, and only 5% are genuine exceptions that need senior judgement.
What this changes
Two things happen when you deploy this properly:
- Close compresses. Not by a day. By a week. Reconciliations stop being the long pole because they stop happening at the end.
- The controls conversation changes. Once the agent is doing the matching, finance has the time — and the data — to actually look at the exceptions. The work shifts from "did everything tie?" to "why does this keep happening?" That's the work the team was hired for.
The trap to avoid
Do not let this become a tooling project. Reconciliation platforms have existed for twenty years and most of them solve 30% of the problem. The leverage is in the investigation and drafting layer — the part that previously required a human to understand the business context.
If your pilot scope is "automate the matching", you've scoped it too small. The matching was never the bottleneck. The thinking after the matching was.