AI automation for financial services, with an approver on every posting
Finance teams do not need an agent that moves faster than they can check. They need one that does the reading, the matching and the investigating, then presents a decision to a named person and records that it did. That is the shape of every system we build in this sector.
- Ingestledger and bank feed
- Matchidentify records
- Investigateexplain the gap
- Approvenamed human
- Postwrite and log
The constraints that actually bind
Someone has to be accountable for the entry
It is not enough that a posting was correct. A named person has to have approved it, and that has to be demonstrable months later. Any automation that cannot produce an approver for every write is unusable here regardless of how accurate it is.
Reconstruction matters more than speed
The question an auditor asks is not how fast the close ran, it is why this specific entry looks like this. A system that cannot answer that creates work rather than removing it, and the answer has to come from records rather than from someone’s memory.
Client data has a hard boundary
What may be sent to a third party model provider is often narrower than teams assume, and the answer differs between a regulated entity and its own clients. This has to be decided before a build starts, because it shapes which steps can use which models.
What we build for finance teams
Almost all of it is operations agent work, shaped by the accountability requirements above.
Continuous reconciliation instead of a month end scramble
The agent runs daily against ledger and bank feed, so drift surfaces the day it appears. By the time the close arrives, the exceptions have already been investigated and most have been resolved.
Exception investigation, not exception reporting
A mismatch triggers a search for the explanation: a payment under a different reference, a fee, a partial settlement, a timing difference. What reaches your team is a proposed resolution with its evidence, not a line on a list.
Every posting held for a named approver
The gate shows the entry, the accounts affected, the before and after, and the evidence. Approval is recorded against a person. That record is the audit trail, produced as a by-product of the workflow rather than assembled afterwards.
Intercompany and multi-entity matching
Where entities transact with each other, the agent matches both sides and flags where they disagree, which is usually a timing or classification difference rather than an error, and is usually found far too late.
A close checklist that updates itself
The status of each reconciliation, what is outstanding, what is waiting on approval, and what was resolved. Visible to the whole team rather than held by the person running the close.
Nothing posts without a person
This is the sector where our default matters most. An agent can read every system you have, match across all of them and investigate every gap, and it still cannot post an entry. The gate holds until a named person approves the specific change, and declining is recorded with its reason alongside approving. The result is that the failure mode is a rejected proposal rather than an entry somebody finds during an audit.
How approval gates workWhat finance teams actually run
These are the systems we integrate with most often in this sector.
- Xero
- QuickBooks
- NetSuite
- Sage
- Stripe
- Bank feeds and BACS files
- PostgreSQL
- SFTP and CSV drops
Where to go next
Questions about this
Can this touch our general ledger directly?
Only through an approval gate, and only for the operations you grant. Most finance teams run read only for the first month, compare the agent’s proposals against what they would have done, and enable writes once that comparison is boring.
What does an auditor actually see?
Does client data go to a model provider?
How does this handle a restatement or a correction?
Start with one reconciliation
Pick the one that costs your team the most days each month. Two weeks later you can watch an agent run it on seeded data.