AI use case · Is your data ready for financial close and FP&A?Automate the close and you automate its errors.
Close automation and AI for FP&A promise days back every month. They act on whatever the ledger says. When sub-ledgers don’t tie, charts of accounts drift between entities and manual journals have no owner, the tool learns to clear differences a person used to question.
Starts with a 30-minute call. Commits you to nothing.
What it needs
What financial close and FP&A needs from your data.
Each requirement, the systems it lives in, and what we check. The AI is the easy part; this list is the hard part. Each check feeds the Truth Score™, a score out of 100 for how far your data can be trusted.
Truth Score pillars that gate it mostGovernanceConsistencyThe six pillars
Where it breaks
Every system looks fine. The gaps between them don’t.
Where financial close and FP&A breaks: not inside one system, but where two of them disagree about the same customer, deal or invoice.
-
Billing ↔ Finance
Revenue in billing that doesn’t match revenue posted
What it does to the AIAn AI reconciliation tool learns to clear the difference instead of explaining it.
-
Payroll ↔ Finance
Payroll journals that don’t match payroll reports
What it does to the AIAccruals get automated on the wrong numbers.
-
Spreadsheets ↔ Finance
Board-pack figures adjusted outside the ledger
What it does to the AIThe pack can’t be reproduced, so it can’t be automated.
Gaps to test for this use case, not client findings.
Before you sign
Questions to ask the vendor selling it.
Four questions that separate a demo from a deployment. Bring the answers, or the proposal, to the Truth Score Read and we test them against your records.
- Q1
When a reconciliation doesn’t balance, does the tool flag it or propose a clearing entry?
A tool that clears differences automatically can hide exactly the errors your auditors will look for.
- Q2
How does it handle different charts of accounts across our entities?
Mapping is where consolidation errors start. Ask who maintains the mapping and how changes are logged.
- Q3
Is every automated entry logged with its source and an approver?
If not, you have traded a slow close for an unauditable one.
- Q4
Can it reproduce last quarter’s board pack from the ledger alone?
If it can’t, the pack depends on adjustments nobody has written down, and those are what you need to find first.
How a Truth Score Read tests it
Financial close and FP&A, tested against your own records.
Our engine matches the customers, people, deals and invoices across the exports from the systems behind the decision. Every red flag is checked by hand against sample records before sign-off, and the verdict comes in writing. Here is what that means for this use case.
- 01
Tie the ledgers
Trial balance, sub-ledger and billing exports compared at period end, entity by entity.
- 02
Test the structure
Chart of accounts and dimension usage compared across entities, and intercompany balances netted.
- 03
Read the journals
Manual journals checked for an owner, a description and timing, so you can see where judgement lives.
- 04
Give the verdict
Which parts of the close can be automated now, which after fixes, and which should stay with a person.
Two systems are enough to start. We send the exact field list after the call.
How the Truth Score Read worksWhat you get back
See what comes back: a real sample memo.
One fictional B2B software company, Example Co: 72,837 records in 7 exports from its CRM, finance and HR systems, run through the same engine and the same checks as a paid Truth Score Read. Each system passes its own checks. Matched across systems, only 13% of deals tie to an invoice.
Questions
Financial close and FP&A, answered plainly.
Anything else, ask on the 30-minute call. It commits you to nothing.
Book the callWhy is governance the gating pillar for automating the close?
Because automation removes the person who used to notice. If mappings, adjustments and journals have no owner and no trail, an automated close produces numbers faster without anyone being able to say why they are right.
Can AI speed up FP&A if our data is messy?
It can speed up the mechanics, but it will also speed up the mess. The sensible order is to find where the ledger, sub-ledgers and spreadsheets disagree, fix the material gaps, then automate.
How do we know which parts of the close are ready to automate?
Test each step separately: the reconciliations that already tie are usually candidates now; intercompany and consolidation usually need work first. The Leverability read gives that call step by step.
Do you replace our auditors?
No. We are not auditors and give no audit opinion. We test whether your data can support the automation you are considering, which is a different question from whether the accounts are true and fair.
Is your data ready for financial close and FP&A? Find out before you sign.
Exports from two or more systems in. A signed verdict on this use case within 5 working days of complete exports. Starts with a 30-minute call. Commits you to nothing.
Starts with a 30-minute call. Commits you to nothing.