AI in Accounting and Audit
Classification, reconciliation and workpaper drafting on infrastructure you control.
Accounting has more structured, rule-bound, high-volume work than almost any other profession, which makes it unusually well suited to automation. The blocker is rarely capability. It is that client financial data sits under engagement-letter confidentiality terms that a public API endpoint does not satisfy. Senteras deploys the models inside the firm.
Book a free consultationWhat makes this hard
- Busy season capacity is a hard ceiling: the work arrives in a ten-week window and hiring cannot flex to match it
- Transaction classification and reconciliation consume junior hours that clients increasingly refuse to pay for
- Client financial data under engagement-letter confidentiality cannot be sent to a third-party model without a conversation most firms do not want to have
- Audit documentation standards require you to explain the basis for a conclusion, which rules out tools that cannot show their work
Where AI earns its place
Transaction Classification
Categorise general ledger activity against the client's own chart of accounts and prior-year treatment, rather than a generic taxonomy that gets it wrong on anything unusual.
Most of the coding work done before a human opens the fileReconciliation and Exception Surfacing
Match across systems and surface only genuine breaks, with the matching logic visible so a reviewer can see why two items were paired.
Review time spent on exceptions, not matchesAudit Sampling and Testing Support
Risk-weighted sample selection with documented rationale, plus first-pass tie-out of supporting documentation to the population.
Defensible selection rationale in the workpaperWorkpaper and Memo Drafting
Draft technical memos and workpaper narratives from the firm's own prior conclusions on comparable facts.
First drafts, reviewed rather than writtenFigures are drawn from Senteras engagements and are illustrative of typical results. Outcomes vary by data quality, infrastructure and scope.
The rules that shape the build
These are the constraints that decide the architecture, usually before anyone has picked a model. This is general information about the regulatory landscape, not legal advice on your obligations.
AICPA Code of Professional Conduct, confidential client information
Disclosing client information to an outside service requires the client's consent. A self-hosted model is not an outside service, which is why the deployment model matters more here than the model choice.
PCAOB and AICPA documentation standards
The workpaper must let an experienced reviewer who was not on the engagement understand the basis for conclusions. A model output with no traceable inputs fails that test.
IRS Circular 230
Due diligence obligations on tax positions do not transfer to a tool. Anything the model produces on a filing position is a draft for a practitioner to verify.
The model never writes the conclusion
In every accounting deployment Senteras builds, the model assembles evidence and drafts narrative but does not reach the conclusion. That is a deliberate design constraint: professional standards require the engagement team to own the judgment, and a system that quietly makes the judgment for them creates a documentation problem that only surfaces during peer review.
Where this applies
The same core systems, with the differences that matter in each setting.
- Public accounting firms
- Busy-season capacity is the driver; the payback is measured against contractor and overtime spend, not headcount.
- Tax preparation practices
- Source document extraction from the client organizer, where the input is a shoebox of PDFs and photos.
- Internal audit departments
- Continuous control monitoring rather than periodic sampling.
- Bookkeeping and outsourced accounting
- Transaction coding at volume; margin depends almost entirely on how much stays automated.
Common questions
Can we use this on client data under our engagement letter?
If the model runs on firm infrastructure, no outside service receives the data and the confidentiality question is straightforward. If you are considering a cloud service, the AICPA confidentiality rule points at needing client consent, which most firms would rather not request one client at a time.
Will a reviewer accept AI-assisted workpapers?
Only if the workpaper documents the basis for the conclusion the way any other workpaper would. Our builds record what evidence the model assembled and what it produced, and the engagement team reaches the conclusion. A system that reaches conclusions creates a documentation problem that surfaces at peer review.
Does this help with busy season capacity?
That is usually the business case. The comparison to make is against contractor and overtime spend in the ten-week window, not against headcount, because the constraint is capacity in a fixed period rather than annual cost.
What about tax positions?
Circular 230 due diligence does not transfer to a tool. Anything the model produces on a filing position is a draft for a practitioner to verify, and we build it to present as one.
How we build it
Local & On-Prem LLM Deployment
The most powerful AI models, running entirely on your hardware.
Custom AI Agents & Automation
AI that doesn't just answer questions. It gets things done.
Model Fine-Tuning & Integration
Models that speak your industry's language, trained on your data.
Document Processing
Extraction, classification and validation across formats you do not control.
Invoice Processing
Reads any layout, and tells you when it is unsure.
AP Automation
Handles the invoice formats your template-based OCR cannot.
Start with a conversation, not a proposal
Thirty minutes. We will tell you what we would change first, and whether you need us at all.
Book a callThe firm behind the firm