Applied AI for Finance and Accounting
Finance is the function with the most repetitive, rule-bound, high-volume work in any company, and it is usually the function running on the oldest process. Month-end close is a manual relay. Reconciliations are done in spreadsheets against system extracts. Forecasts are stale the day they are published. In our experience none of that comes down to a technology gap. It comes down to the distance between what the systems hold and what anyone has had the hours to do with it.
In short
Mach12 stands up AI labs that apply AI inside finance and accounting functions: close acceleration, reconciliations, AP and AR automation, forecasting, audit preparation, and management reporting. The lab connects directly to the ERP and the sub-ledgers so the work happens against live data rather than an export.
Where Finance Loses Time
These are the patterns we see most. In our experience they come down to the distance between what the systems already hold and what anyone has had the hours to do with it, rather than to a technology gap.
Close is a manual relay race
Accruals, intercompany, allocations, and reconciliations move person to person on a checklist. Each handoff is a wait state, and the cycle is rebuilt from scratch each period.
Reconciliations happen in spreadsheets
Analysts pull extracts, match them by hand, and investigate the breaks. The matching is mechanical and the investigation is where the judgment sits, but the mechanical part consumes the week.
Forecasts are stale before they are read
Rolling forecasts depend on inputs from a dozen owners collected over two weeks. By the time the picture is assembled, the assumptions underneath it have already moved.
Audit preparation is a fire drill
Sample requests, supporting documentation, and control evidence are assembled reactively. The evidence exists across systems, but nobody has pulled the thread until the auditor asks.
Reporting answers last month, not this one
Management reporting runs as a production process rather than an answer service. Getting a new cut of the numbers means a ticket and a wait, so people stop asking questions they would benefit from asking.
AP and AR exceptions eat the team
The straight-through volume is fine. It is the exceptions, the mismatches, the short pays, and the coding questions that absorb the headcount, and those are the cases that need context from other systems.
What the Lab Builds
Built against your systems and your data, shipped into production with the people who use them. A given lab will build a subset of this, in whatever order discovery ranks it.
Close acceleration
Agents that run the mechanical parts of the close and escalate only what needs a person.
- Automated account reconciliation with break classification and routing
- Accrual and allocation preparation from source system activity
- Intercompany matching and elimination support
- A live close dashboard that shows what is blocking rather than what is late
Forecasting and analysis
Forecasts assembled from system activity continuously instead of collected from owners periodically.
- Driver-based forecast assembly from live ERP and operational data
- Variance explanation drafted against actuals, with the drivers named
- Scenario modeling on demand instead of on request
- Cash forecasting from AR aging, AP terms, and commitment data
Transaction processing
Exception handling in AP and AR, where the volume and the context problem both live.
- Invoice coding and three-way match exception resolution
- Short pay and deduction research across order, ship, and invoice data
- Vendor and customer inquiry response drafted from system of record
- Duplicate and anomaly detection ahead of payment
Audit and controls
Evidence assembled continuously so audit preparation stops being an event.
- Continuous controls testing against live transactions
- Sample selection and supporting documentation assembly
- Policy and accounting treatment questions answered from your own guidance
- Exception and override monitoring with a documented trail
Typical First Builds
Chosen for speed to production as much as for value. We would rather have something working in your environment early than something more ambitious on paper.
- 01The reconciliation that costs the most analyst hours each month
- 02Variance explanation drafting for the management reporting pack
- 03AP exception triage on the highest-volume vendor population
- 04A question-answering agent over the ERP for the finance team, read-only to start
Built Against What You Run
Connectors are built during stand-up. You do not replace anything first, and your data does not leave your boundary.
Relevant Accelerators
Applications we have already built in this area. A lab can deploy one as-is, extend it, or use it as the pattern for something new.
Common Questions
- Will AI be making journal entries in our ledger?
- Only where you decide it should, and only behind the same approval and segregation of duties controls your people work under. Most labs start read-only: drafting, matching, and explaining, with a human posting. Write access gets added deliberately, use case by use case, once the evaluation results justify it.
- How do you handle auditability?
- Each action an agent takes is logged with its inputs, its reasoning, and its output. That trail is built during stand-up, before any use case ships. Our view is that retrofitting it later leaves organizations unable to explain their own controls.
- Our data is not clean enough for this. Is that a blocker?
- We would treat it as a scoping input. Data readiness is part of how we score use cases, so the first builds are chosen where the data supports them. Several finance use cases improve data quality as a side effect, since the exceptions finally get surfaced and fixed.
- Do we need to replace our ERP first?
- No. The lab connects to what you run today. If an ERP modernization is already on the roadmap we can work either side of it, and the connectors get rebuilt against the new system when you cut over.
- How quickly would we see something working?
- The lab is stood up in the first few weeks and the first finance build typically ships inside the first quarter. We choose the first use case partly on how fast it can get to production, because momentum matters more than ambition at the start.
Common in these industries
The work carries across industries. These are where we see it most often.
Build This in Your Business
Tell us what your finance & accounting function runs on and where the work is piling up. We will scope the first build.
Start a Lab