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Field guide

AI finance-operations automation

Reconciliation, AP/AR, invoice processing and month-end close, automated with a human-in-the-loop review path finance can actually sign off.

All insights

Most of finance's cost is not analysis, it is operations: matching bank lines to the ledger, keying invoices, chasing cash, and closing the books. These are high-volume, exception-driven workflows where deterministic matching does the bulk of the work and an LLM handles the messy tail. That is the reverse of the demo-driven approach that stalls in production.

This guide covers the automation layer for accounting and finance operations: AP invoice capture and three-way matching, bank and intercompany reconciliation, AR cash application and collections, and close acceleration, each with the straight-through-processing thresholds, exception routing and audit trail that let a controller trust the numbers.

finance operations automationreconciliation automationAP invoice automationmonth-end close automationcash applicationstraight-through processingAR collections AI

Current signals

As of June 2026
  • Straight-through processing targets are rising: leading AP teams auto-post the majority of clean invoices and route only the exception tail to people.
  • Deterministic matching still clears most reconciliations; the LLM earns its place on the messy remainder, not the whole ledger.
  • Continuous close is replacing the month-end scramble, with reconciliations and accruals running as data arrives.
  • Every automated posting now carries an audit trail and a human sign-off gate where the amount or risk crosses a threshold.

In this guide

#idp

Intelligent document processing for finance, end to end

IDP is more than OCR. Here is the classify, extract, validate and route pipeline we build so finance documents flow without a keying team.

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#categorization

Building a transaction categorization engine

Categorising transactions powers budgeting, underwriting and reconciliation alike. Here is the model and feedback loop we build for accurate categories.

#audit

Automating audit workpapers and evidence

Audit is evidence collection at scale. Here is how we automate sampling, tie-outs and workpaper assembly while keeping the trail a reviewer trusts.

#treasury

AI for treasury cash positioning and forecasting

A treasurer needs tomorrow's cash position, not last month's. Here is the categorisation and short-horizon forecasting we build for treasury.

#revenue recognition

Automating revenue recognition under ASC 606

Rev rec turns messy contracts into scheduled revenue under strict rules. Here is where AI extracts the terms and where a controller must still decide.

#billing

Automating subscription and usage billing

Usage billing breaks on proration, upgrades and disputes. Here is the billing and reconciliation automation we build so revenue ties out.

#payments

Automating payment-operations exceptions

Payments succeed until they do not, and the exceptions eat the team. Here is how we triage returns, repairs and mismatches with a review path.

#master-data

Cleaning vendor and customer master data with AI

Duplicate and mismatched master records cause duplicate payments and broken matching. Here is the entity-resolution approach we use to clean them.

#fixed-assets

Automating fixed-asset accounting

Fixed assets accrue depreciation, disposals and impairments nobody wants to track by hand. Here is how we automate the subledger with a review path.

#spend-controls

Spend controls and approval routing with AI

Approval chains are where spend policy meets reality. Here is how we automate routing and enforce controls without stalling the business.

#tax

Automating the tax provision with AI support

The tax provision is mostly reconciliation and classification under a hard deadline, with documentation to defend every number. Here is where AI shortens it and where a preparer must still own the result.

#procurement

Automating purchase orders end to end

The PO is where spend gets controlled or lost. Here is the requisition, approval and matching automation we build so procurement scales without chaos.

#vendor onboarding

Automating vendor onboarding and verification

Onboarding a vendor means verification, tax forms and bank details fraudsters target. Here is the automation and controls we build around it.

#contracts

Contract intelligence for procurement and finance

Obligations, renewals and price terms hide in PDFs nobody rereads. Here is the extraction and monitoring layer we build over a contract portfolio.

#insurance

Insurance claims triage automation with false-positive control

Claims triage has to move quickly without paying claims that were never covered or dragging honest claimants into investigation. Here is the document-extraction and scoring design we build for a regulated claims workflow.

#intercompany

Intercompany reconciliation: matching across entities and currencies

Intercompany breaks hide in timing, FX and mismatched references across ledgers. Here is the entity-resolution and matching approach we use to clear them before close.

#audit

Automating expense and invoice audit with AI

Expense audit is sampling because humans cannot check everything. Here is how we score every line for policy breaches and duplicate or fraudulent claims.

#automation

AR automation: cash application and collections that learn

Applying a payment with no clean remittance is a matching problem, and collections is a prioritisation problem. Here is how we automate both with a review path.

#automation

Three-way matching AI: PO, receipt and invoice without the manual chase

Three-way match breaks on partial deliveries, unit mismatches and split invoices. Here is the matching logic and exception routing we build so AP does not stall.

#financial-close

Accelerating the month-end close with AI, without losing control

The close is a dependency graph of reconciliations, accruals and reviews. Here is where AI actually shortens it and where a human must still sign the number.

#reconciliation

Bank reconciliation automation: deterministic matching first, LLM for the tail

Most reconciliation should never touch a model. Here is how we split exact matching from the fuzzy exceptions an LLM handles, with an audit trail a controller trusts.

#automation

AP invoice automation: an architecture that survives the messy tail

Invoice capture demos look easy until real vendor formats arrive. Here is the extraction, validation and human-review architecture we use to hit high straight-through rates.

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