Reconciles data from the sources you already use:
- Bank accounts
- Stripe
- PayPal
- Shopify
- Amazon
- Uploaded statements
Approved entries post to your ERP.
What is AI reconciliation software?
AI reconciliation software automates financial reconciliation in two layers. Rules match the records that clearly agree, and AI investigates the records that don't, either explaining each difference with evidence or flagging it as an exception for a person to review.
Automated reconciliation software usually stops at the first layer. AI-powered reconciliation adds the second, which is where finance teams spend most of their reconciliation time. For a deeper explanation, read our guide to what AI reconciliation software is and how it works.
Greenloom AI reconciliation software at a glance
- What it is
- AI-powered reconciliation software that runs alongside your ERP
- What it reconciles
- Bank transactions and payment, payout and settlement records against your ERP
- Data sources
- Bank accounts, Stripe, PayPal, Shopify and Amazon, plus uploaded statements
- The rules layer
- Matches clean records on defined fields, with a tolerance for rounding
- The AI layer
- Investigates unmatched records and explains each difference with evidence
- Human control
- Every match and explanation waits for a named person to approve it
- Audit trail
- Receipts for every decision, including the AI's, with names and times
- Setup
- A 30-minute call to reconcile one month of your data live, with no ERP changes
- Pricing
- One flat monthly fee, free for 20 days with no card needed
What is the difference between manual, automated and AI-powered reconciliation?
Manual reconciliation relies on people and spreadsheets. Automated reconciliation software matches records with rules. AI-powered reconciliation adds investigation, so the records rules can't match are explained instead of piling up.
| Criteria | Manual reconciliation | Automated reconciliation software | AI-powered reconciliation |
|---|---|---|---|
| How records are matched | By eye, VLOOKUPs and tick marks | Rules pair records when fields agree | Rules first, then AI for what rules miss |
| Vague memos and missing references | Emails to customers | Usually left unmatched | Matched on customer, date and amount when evidence supports it |
| Net payouts and short payments | Worked through by hand | Fail the rule every time | Broken into fees, refunds, tax, FX and bank charges |
| What reaches your team | Everything | A long exception list | A short list of real exceptions, with evidence |
| Upkeep | Grows with volume | Someone writes and maintains rules | Fewer hand-written rules |
| Audit trail | Spreadsheets and email | Match log | Matches, explanations, evidence and approvals |
Why do finance teams need reconciliation automation software?
Finance teams need reconciliation automation software because money now arrives through more channels than people can match by hand, and each channel pays net, late and in bundles. Rules alone can't keep up, so exceptions pile up until the close slips.
Vague memos
A deposit arrives with a reference like "TRF 0382" and no invoice number.
AI can investigateLump-sum payments
One customer payment covers several invoices, or one payout covers hundreds of orders.
AI can investigateShort payments
Fees, withholding tax, FX movements and bank charges come out before the money lands.
AI can investigateTiming and splits
Payments arrive in a later month, or a single payout is split across two batches.
AI can investigateReal errors
Duplicate payouts, fees above your contract rate and missing entries need a decision.
Needs a personSigns you've outgrown rules-based matching
Finance teams are adopting AI for exactly this work. In Gartner's 2025 survey, 59% of finance leaders said they use AI in their finance function, and error and anomaly detection was one of the top three uses.
See the AI on my data- Your exception list is longer at month end than it was a year ago.
- Someone maintains matching rules that break whenever a format changes.
- Unidentified payments sit in a suspense account for weeks.
- Your team still exports data to spreadsheets to investigate what didn't match.
- Nobody checks fees and deductions against your contract rates.
What features should AI reconciliation software have?
AI reconciliation software should match records across every system, investigate what doesn't match, explain each difference with evidence, and keep people in control of what posts. Here's how Greenloom covers each of those.
Multi-system reconciliation
Bank accounts, Stripe, PayPal, Shopify and Amazon are reconciled against your ERP in one place, every day.
A two-layer matching engine
A rules engine handles clean one-to-one, one-to-many and many-to-many matches. The AI layer takes everything else.
AI that reads unstructured data
The AI interprets vague bank memos, payout files and partial references, and links lump-sum deposits to what's inside them.
Evidence-backed explanations
Every explanation names the reason and the record that proves it, such as a settlement line, a fee schedule or a bank charge.
Short-payment breakdowns and fee checks
Short payments are split into fees, withholding tax, FX movements, bank charges and refunds, and fees are checked against your agreed rates.
Duplicate, late and split detection
Duplicate references, late payouts and payouts split across batches are held with a plain-English reason.
Structured exception tracking
Only unexplained items reach a person, and each one has an owner, an age and its evidence attached.
Human-in-the-loop approvals
The AI proposes; your team decides. Approved entries post to your ERP, and nothing posts without a yes.
Daily, continuous reconciliation
Every transaction is reconciled every day instead of at month end, so problems surface while they're small.
Receipts for every decision
The audit trail records what matched, what the AI explained, what was held, who approved it and when.
Which reconciliation software features are must-haves?
| Feature | Must-have or nice-to-have | Why it matters |
|---|---|---|
| Integration with your ERP, banks and payment providers | Must-have | Without clean data in, nothing else works |
| Rules-based matching with tolerances | Must-have | Clears the easy matches predictably |
| Investigation of unmatched records | Must-have | This is where most reconciliation hours go |
| Approval before posting | Must-have | Keeps a named person accountable for every entry |
| Time-stamped audit trail | Must-have | Your auditor needs evidence and an approver |
| Fee checks against contract rates | Nice-to-have, high value | Turns overcharges into money you can recover |
| Exception ownership and aging | Nice-to-have, high value | Stops exceptions from turning into write-offs |
| Dashboards and reports | Nice-to-have | Shows progress, but doesn't do the work |
How does AI-powered reconciliation work?
AI-powered reconciliation works in six steps. The software connects your data, standardizes it, matches clean pairs with rules, investigates the rest with AI, explains or flags each difference, and posts only what your team approves.
Connect your data
Connect your banks, Stripe, PayPal, Shopify and Amazon, or upload statements on day one. Your ERP stays as it is.
Standardize every record
Dates, amounts, currencies, references and transaction IDs are put into one format so they can be compared.
Match with rules
The rules engine pairs records that clearly correspond. It's predictable and repeatable, which auditors like.
Investigate with AI
The AI examines every unmatched record against settlement reports, fee schedules, refunds, tax and references.
Explain it or flag it
When evidence supports a reason, the difference is reconciled and the reason recorded. Otherwise, it becomes an exception.
Approve and post
Your team clears a short queue. Approved entries post to your ERP, with a name and time on every decision.
Is agentic AI the future of reconciliation?
Agentic AI is already changing reconciliation, but only when it stays accountable. An AI agent can run a whole investigation on its own: pull the settlement file, check the fee schedule, test a refund and report what it found. That saves hours.
The risk is an agent that acts without showing its work. Microsoft notes that its own Copilot reconciliation suggestions can be incorrect or incomplete, which is why a person reviews them. Greenloom's AI works the same way:
- It shows the evidence for every match and explanation.
- It never guesses a reason it can't prove.
- It never posts to your ledger without approval.
What can AI reconciliation software solve that rules can't?
AI reconciliation software solves the cases that break rules: short payments, missing references and payments split across batches or periods. Choose a scenario to see how Greenloom's AI handles it.
| Line | Amount | Evidence | Result |
|---|---|---|---|
| Invoice INV-9120 in the ERP | $100,000.00 | Open invoice | Matched |
| Withholding tax | −$15,000.00 | Tax certificate from the customer | Explained |
| FX movement between invoice and payment | −$5,850.00 | Exchange rates on both dates | Explained |
| Bank charges | −$250.00 | Charge lines on the statement | Explained |
| Payment received | $78,900.00 | Bank statement | Fully reconciled |
What your team sees
Instead of a $21,100 shortfall to chase, your team sees three named deductions with the evidence for each, ready to approve.
Rules-based matching would have left the whole payment unmatched.
| Line | Amount | Evidence | Result |
|---|---|---|---|
| Expected payout from the processor | $31,780.00 | Payout report | Expected |
| First deposit, March 8 | $19,600.00 | Bank statement | Matched |
| Second deposit, March 9 | $12,180.00 | Bank statement | Matched |
| Total received | $31,780.00 | Both halves linked to one payout | Fully reconciled |
What your team sees
One payout, two deposits, both halves matched. Before the second deposit arrived, the first was held with the reason "split payout, waiting for the second batch."
Rules would have flagged two unmatched deposits and one missing payout.
| Line | Amount | Evidence | Result |
|---|---|---|---|
| Payout reference 8842-B, first posting | $19,400.00 | Bank statement and payout report | Matched |
| Payout reference 8842-B, second posting | $19,400.00 | Same reference already matched | Held as duplicate |
| Result | Original posts, duplicate waits | 1 item for review |
What your team sees
The original posts after approval. The duplicate is held with the reason "duplicate payout reference," so it never inflates your revenue or cash.
Your team decides whether to reverse it or contact the provider.
See the AI work on your own data
Bring one month of bank and payout statements. In a 30-minute call, we'll reconcile it live and show you every match, explanation and exception.
Why do finance teams choose Greenloom's AI reconciliation software?
Finance teams choose Greenloom because its AI does the investigation, people keep control, and every decision comes with a receipt.
The AI does the work
Greenloom doesn't just track reconciliation tasks. Its AI investigates each unmatched record and prepares the answer, so your team reviews instead of researching.
People stay in control
Your team sees the AI's reasoning and the source data before approving. Nothing posts to your ERP without a named person's yes.
Every decision has a receipt
Matches, explanations, holds and approvals are logged with names and times, so your auditor can trace any number back to its source.
What doesn't Greenloom's AI do?
Finance teams should know where an AI tool stops. Here's where Greenloom stops.
- It doesn't replace your ERP or general ledger. It works alongside them.
- It doesn't post a journal entry without approval from your team.
- It doesn't invent explanations. Without evidence, a difference stays open for a person.
- It doesn't replace your controller's judgment or your auditor's review.
Is AI-powered reconciliation audit-ready?
AI-powered reconciliation is audit-ready when every AI decision traces back to source evidence, a named person approves what posts, and the full trail is available to your auditor.
Auditors care about what supports a number and who checked it, not which tool produced it. For US public companies, Section 404 of the Sarbanes-Oxley Act requires management to maintain and assess internal control over financial reporting. In 2024, the PCAOB updated its standards on technology-assisted analysis, which puts more weight on whether the data behind the numbers is reliable.
Many companies also assess AI tools against the voluntary NIST AI Risk Management Framework before rollout.
Questions to ask any AI reconciliation vendor
- Can we see the evidence behind every AI match and explanation?
- Can the AI write to our ledger without approval?
- Where is our data stored, how is it encrypted, and is it used to train models for other customers?
- Can our auditor get read-only access to the trail?
We answer each of these questions in writing on the first call.
What the audit trail looks like
- Matched $19,600 Stripe payoutMatched
- Held: fee 8.5% over agreed rateException
- Approved: tax variance within toleranceApproved
- Held: duplicate payout referenceException
- Split payout: both halves matchedMatched
How does Greenloom compare with other reconciliation automation software?
Reconciliation automation software falls into five types. Each one puts the work in a different place, so the right choice depends on where your team's hours actually go.
| Type | Where the work sits | Best fit when |
|---|---|---|
| ERP reconciliation (NetSuite, QuickBooks, SAP, Oracle, Microsoft Dynamics, Odoo) | Matches bank lines to items already in the ledger, increasingly with AI suggestions | You have few payment channels and payments carry clear references |
| Rules-based reconciliation tools | Pair records by configured rules and list what doesn't match | Your data is clean and formats rarely change |
| Financial close platforms | The control workflow: proving balances are right and that the right people signed off | Your main pain is certification across many balance sheet accounts |
| AI-native ERPs | Rebuild the general ledger itself; adopting one means replacing your ERP | You're replacing your ERP anyway |
| AI reconciliation layer, such as Greenloom | Runs alongside your ERP and investigates the transactions that didn't match | Short payments, missing references and exceptions pile up every month |
Do you need AI reconciliation software if your ERP already has AI?
Often, yes. ERP AI features usually suggest matches for items already in the ledger. They do less with the question that comes first: what a payment actually is when it arrives net, late, split or without a reference. We look at one ERP's recent changes in what Odoo 20's reconciliation update fixes and what it still can't do. For payout-heavy businesses, see our payment reconciliation software.
How do you evaluate AI reconciliation software?
To evaluate AI reconciliation software, score each vendor on the same eight criteria and test them on your own hardest transactions, not their demo data.
| Criterion | What to ask | What good looks like |
|---|---|---|
| AI and matching intelligence | Does it handle vague references, partial and lump-sum payments? Does it explain its matches? | Every AI match shows its evidence, and unexplained items stay open |
| ERP, bank and payment integration | Does it work with our ERP, banks and processors without changes? | Connects to your current systems, with file uploads on day one |
| Exception handling | How are exceptions routed, owned and aged? | A short queue with owners, ages and evidence attached |
| Currency and multi-entity support | How are FX differences and multiple entities handled? | FX movements appear as their own explained line |
| Audit readiness and controls | Is there read-only auditor access, segregation of duties and time-stamped logs? | A named approver and timestamp on every entry |
| Data security | Where is data stored, how is it encrypted, and is it used for model training? | Clear answers in writing before you connect live data |
| Total cost of ownership | How is it priced at next year's volume? | Predictable pricing that doesn't punish growth |
| Time to value | How fast can we see results on our own data? | Results on your data in the first meeting |
What are the best alternatives to Excel for reconciliation?
The best alternative to Excel depends on your volume and data. ERP reconciliation tools suit low volumes with clean references. Rules-based tools suit predictable data. AI reconciliation software suits teams with several payment channels, short payments and growing exception lists, because it investigates what rules can't match.
Before you choose, read our guide to financial reconciliation for the basics of what any tool needs to cover.
Bring three test cases to every demo
- A payment that arrived short of the invoice
- A bank deposit with a memo that means nothing
- A payout split across two deposits
Watch for these red flags
- AI that can't show why it made a match
- "Fully autonomous" posting to your ledger
- Match rates quoted without saying how they're measured
- Demos that only use the vendor's sample data
Who uses AI reconciliation software?
Finance teams use AI reconciliation software when they're paid through several channels at once and their close is held up by exceptions rather than by matching.
By industry
E-commerce and direct-to-consumer brands
Stripe, PayPal and Shopify payouts, Amazon settlements, refunds and chargebacks across several sales channels.
Restaurants and food service
POS sales, delivery app payouts, commissions and card deposits that rarely agree line for line.
Consumer packaged goods
High volumes, distributor and retailer deductions, and payments that arrive weeks after the invoice.
By reconciliation type
- Bank reconciliation
- Payment and settlement reconciliation
- Card processor payouts
- Marketplace settlements
- Customer payments without references
- Cross-border receipts
- Split and batched payouts
By role
| Role | What changes for them |
|---|---|
| Controller | Exceptions arrive already investigated, so the close stops waiting on research. |
| Head of accounting or finance operations | A short exception queue with owners replaces a growing backlog. |
| CFO or VP of Finance | Numbers hold up to the board and the auditor, and they're ready sooner each month. |
| Finance transformation lead | A measurable automation project that doesn't require replacing the ERP. |
What are the benefits of AI-powered reconciliation?
AI-powered reconciliation gives your team fewer exceptions to research, a faster close and an audit trail that explains itself.
Fewer exceptions to research
The AI investigates unmatched records first, so your team only sees what the evidence can't explain.
A faster month-end close
Differences are explained every day, so they don't pile up and hold up the close.
Less rule maintenance
The AI layer covers the variation that used to need a new hand-written rule every time a format changed.
An audit trail that explains itself
Every match comes with its reason, its evidence and its approver.
Money recovered
Fees above your contract rate and duplicate payouts are caught instead of absorbed.
Growth without new hires
You can add channels, brands and volume without adding people to reconcile them.
How much time could reconciliation automation save your team?
Move the sliders to match your month. The calculator estimates how many hours your team spends investigating transactions that don't match on the first pass.
- Transactions that need a person
- 500
- Cost per month
- $2,500
- Cost per year
- $30,000
Greenloom's AI investigates these transactions first and sends your team only the ones the evidence can't explain.
Prefer to see real numbers? Reconcile one month of your data live.
How long does it take to implement AI reconciliation software?
With Greenloom, you see the AI working on your own data during the first 30-minute call, and you can run a full close in parallel before you pay anything.
- Day 1
Reconcile a month live
In a 30-minute call, we load one month of your bank and payout statements and walk you through every match, explanation and exception.
- Days 1 to 20
Use it free on your data
Connect your sources or keep uploading statements. You don't need a card, and your ERP doesn't change.
- Your next close
Run one close in parallel
Compare Greenloom's results with your current process and review any differences between the two.
- After that
Go live and add sources
Move each source over once the results agree, then add the next one.
Pricing: one flat monthly fee. Free for 20 days on your own data, with no card needed.
Start my free 20-day trialReconciliation guides and resources
Practical guides from the Greenloom team for controllers and finance operations leads.