AI for Finance Data
and Operations
Getting data out of an ERP was solved twenty years ago. Knowing what conditions it was true under was not.
- A number without its as at time, period status, entity scope and rate basis cannot be defended the moment somebody else uses it.
- Material processes will keep living in spreadsheets. The realistic goal is a tie back to the ledger every period rather than eliminating the workbook.
- A bad transaction is one wrong number. A bad master record generates wrong numbers continuously, usually for years, which is why master data validation pays more.
- Natural language search fails on real ledgers because terms do not resolve and scope is not stated, not because the model is weak.
- An exception routed to a shared inbox is a report. Routing has to reach a named person with the evidence attached.
AI agents for finance data are only as good as the conditions attached to what they read. ChatFin stamps the as at time, the period status, the entity scope, the rate basis and the filter set on every extract, because a figure separated from those conditions becomes indefensible the moment a second person uses it.
The same discipline runs through the rest. ChatFin ties spreadsheet workings back to the posted journal every period, profiles master data against how it is actually used, and refuses to answer a question the data will not support rather than returning a plausible number.
Extraction is easy. Knowing what you extracted is not
Getting data out of an ERP is a solved problem and has been for twenty years. The failure is downstream, where a number that was correct at extraction becomes wrong in use because nobody recorded the conditions it was true under. ChatFin treats those conditions as part of the data.
| What is usually recorded | What is usually not | What goes wrong |
|---|---|---|
| The report name | The as at time | Comparison across two moments |
| The period | Whether it was closed | A figure that changed after |
| The entity | The consolidation basis | Totals that cannot be rebuilt |
| The currency | The rate type and date | Two versions of one number |
| The filters applied | Why they were applied | Nobody can vary the question |
Every one of those turns a defensible extract into an indefensible one the moment somebody else uses it. ChatFin stamps the as at time, period status, entity scope, rate basis and filter set on every extract, which is what makes a number arguable rather than disputed.
ChatFin stamps the as at time, period status, entity scope, rate basis and filters on every extract, because a number without its conditions cannot be defended.
The Excel to ERP reconciliation nobody admits to running
Every finance function has material processes that live in spreadsheets and end in a journal. Revenue schedules, accrual workings, commission calculations, rebate models. They are not going away and pretending otherwise is why data projects fail. The problem is not that they exist, it is that the tie back is manual and annual.
ChatFin reconciles the spreadsheet output against the posted journal every period rather than at year end, and reports where the workbook has changed since the entry was made. That does not eliminate the spreadsheet and it does make it auditable, which is a more realistic goal.
ChatFin ties the spreadsheet output back to the posted journal every period and reports where the workbook changed after the entry was made.
Cross system reconciliation is where the real breaks live
Reconciling within one system is arithmetic. Reconciling between two is where finance actually loses time, because each system is internally consistent and the two disagree for reasons neither can see.
| The pair | Why they differ | What resolves it |
|---|---|---|
| ERP and bank | Value dating and in flight items | Reconcile on event, not balance |
| ERP and billing | Different revenue triggers | Agree the trigger, then compare |
| ERP and payroll | Accrual against payment timing | Compare by pay period |
| ERP and the warehouse | Receipt posted at different moments | Reconcile by document |
| ERP and the tax engine | Different account grouping | Map the grouping once |
| ERP and the CRM | A pipeline is not a contract | Do not reconcile these at all |
That last row is worth stating. Some system pairs should never be reconciled because they record different things, and a great deal of effort goes into forcing agreement between numbers that were never meant to agree. ChatFin says so rather than building the reconciliation.
For the pairs that should agree, ChatFin reconciles at event level rather than balance level, so a difference comes back as the documents that caused it. A balance level reconciliation between two systems tells you that they differ, which everybody already knew.
ChatFin reconciles system pairs at event level and will tell you where a pair should not be reconciled at all, rather than building agreement that was never meant to exist.
Master data validation is worth more than transaction validation
A bad transaction is one wrong number. A bad master record generates wrong numbers continuously until somebody notices, which is usually years. The return on checking master data is far higher and almost nobody does it on a schedule.
| Master record | The defect | How long it runs undetected |
|---|---|---|
| Vendor | Duplicate under a variant name | Years, and it splits spend |
| Customer | Wrong payment terms | Until a collections review |
| Chart of accounts | Two accounts, one purpose | Until a consolidation fails |
| Cost centre | Assigned to a closed entity | Until the entity is audited |
| Item | Wrong valuation class | Until an inventory count |
| Exchange rate | A missing rate date | Until a revaluation looks odd |
None of those is hard to detect and all of them are invisible unless somebody looks. ChatFin profiles master data continuously against how it is actually used, which catches the vendor duplicate that a name comparison misses because the two records share a bank account rather than a name.
Natural language search only works on top of resolved meaning
Asking a finance system a question in plain language is the capability everyone wants and the one most likely to be demonstrated on clean data and fail on yours. ChatFin is direct about why.
The words have to resolve
Revenue means one thing in the management pack and another in the statutory accounts. Until the question knows which, the answer is confident and arbitrary.
The scope has to be stated
Which entities, which period, closed or open, at which rate. A question that omits these is not broad, it is undefined, and a system that answers anyway is worse than one that asks.
The lineage has to survive
An answer that cannot be traced back to the transactions behind it cannot be used in a review, whatever its accuracy.
Uncertainty has to be visible
Where the data does not support an answer, saying so is the correct output. A plausible number with no caveat is the failure mode that damages trust permanently.
ChatFin resolves the term, states the scope, keeps the lineage and refuses where the data will not support the question. That is slower than a demonstration and it is the only version that survives contact with a real ledger.
ChatFin resolves the term, states the scope, keeps lineage back to the transactions, and says so where the data will not support an answer.
Exception routing and human approval are the whole design
Every use case on this page ends in the same place: something needs a person. The quality of that handover decides whether an automation is used or quietly abandoned, and it is the part most tools treat as an afterthought.
ChatFin proposes and a person disposes on every posting, which is a constraint rather than a limitation. It is also what makes the approach acceptable in ERPs where nobody will grant write access to software.
Across the finance data cluster
Where each of the master list use cases is worked.
Hubs live now
Workflows and ownership · Natural language search · ERP data quality and cleansing
Covered as sections here
ERP data extraction · Cross system reconciliation · Excel to ERP reconciliation · Data validation · Master data validation · Finance data quality checks · Automated finance workflows · Exception routing · Human in the loop approvals
Related reading
Finance document intelligence · Finance data and ETL tools compared
Neighbouring functions
Financial reporting · Month end close · FP&A
Questions finance systems teams ask
Why stamp conditions on an extract?
Because the same query run two hours apart, or against an open period rather than a closed one, or at a different rate date, returns a different and equally correct number. Without the as at time, period status, entity scope and rate basis recorded, two people compare two extracts and neither can prove the other wrong.
Are you going to tell us to stop using spreadsheets?
No. Revenue schedules, accrual workings and commission models live in spreadsheets at almost every finance function and that is not changing. ChatFin reconciles the workbook output against the posted journal each period and reports where the workbook changed after the entry, which makes the process auditable without removing it.
Why is master data validation a priority?
Because the return is higher. A wrong transaction is one wrong number, while a duplicate vendor splits spend and defeats every duplicate invoice check until somebody notices, typically years later. ChatFin profiles master data against actual usage, which catches the duplicate that shares a bank account rather than a name.
Why does natural language search fail on real data?
Because the words do not resolve and the scope is not stated. Revenue means one thing in the management pack and another in the statutory accounts, and a question that names neither entity nor period nor rate basis is undefined. ChatFin resolves the term, states the scope and keeps lineage back to the transactions.
Keep the conditions, keep the lineage.
ChatFin stamps the as at time, period status, entity scope and rate basis on every extract, and keeps lineage from any answer back to the transactions behind it.
It ties spreadsheet workings to the posted journal each period, profiles master data against real usage, and routes every exception to a named person with the evidence attached.
Bring one extract and the workbook that feeds a journal.
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Bring one extract and one workbook.
We will run both through ChatFin, tie the workbook to the posted journal, and show you what conditions your current extract is missing.