Matches transactions from:
- Bank accounts
- Stripe
- PayPal
- Shopify
- Amazon
- Uploaded statements
Approved entries post to your ERP.
What is transaction matching software?
Transaction matching software automatically pairs transactions in your ledger with the same transactions in an external record, such as a bank statement, payout report or settlement file. It flags anything that doesn't pair so your team can investigate it.
It's the core of every reconciliation tool. Gartner describes financial reconciliation solutions as the tool that helps controllership teams manage and reconcile financial transactions across internal and external data sources. Greenloom adds a second step: its AI investigates what didn't match and explains it with evidence where it can.
Greenloom transaction matching software at a glance
- What it is
- Automated transaction matching software with AI investigation, running alongside your ERP
- What it matches
- Bank lines, payouts, settlements and customer payments against your ERP
- Data sources
- Bank accounts, Stripe, PayPal, Shopify and Amazon, plus uploaded statements
- Match types
- One-to-one, one-to-many, many-to-one and many-to-many
- Match methods
- Exact, within a tolerance, and on customer, date and amount when there's no reference
- Unmatched items
- Investigated by AI, then explained with evidence or held as exceptions
- How often
- Every day
- Audit trail
- The rule or evidence behind every match, plus who approved it and when
- Pricing
- One flat monthly fee, free for 20 days with no card needed
What is automated transaction matching?
Automated transaction matching is the use of software to compare records from two or more sources and pair the ones that correspond, without anyone checking them line by line. Rules handle the clear matches. AI-assisted matching goes further and works out the ones rules can't.
| Criteria | Manual matching | Rules-based automated matching | Rules plus AI investigation |
|---|---|---|---|
| How records are paired | By eye, filters and lookups in spreadsheets | Software pairs records when set fields agree | Rules pair the clear ones; AI investigates the rest |
| Speed at high volume | Slows down as volume grows | Handles high volumes quickly | Handles high volumes, including the leftovers |
| Missing or partial references | Emails and guesswork | Usually left unmatched | Matched on customer, date and amount when evidence supports it |
| Amounts that differ | Investigated by hand | Matched only within a set tolerance | Differences broken into fees, refunds, tax, FX and charges |
| Errors and anomalies | Found if someone notices | Listed as unmatched | Duplicates and fees above the agreed rate flagged with a reason |
| Reports and audit trail | Prepared by hand | Match logs | The rule or evidence behind each match, plus approvals |
What types of transaction matching are there?
There are four types of transaction matching: one-to-one, one-to-many, many-to-one and many-to-many. The type depends on how many records on each side make up the same amount of money.
| Match type | What it means | Everyday example | Greenloom |
|---|---|---|---|
| One-to-one | One record on each side | A customer pays one invoice in full | Supported |
| One-to-many | One record matches several on the other side | One Stripe or Shopify payout covers hundreds of orders | Supported |
| Many-to-one | Several records match one on the other side | A payout arrives as two separate deposits | Supported |
| Many-to-many | Several records on each side add up to the same total | Three transfers pay off two invoices | Supported |
Which transaction matching methods does Greenloom use?
| Method | How it works | When it's used |
|---|---|---|
| Exact matching | Reference, amount and date agree on both sides | Clean data with shared IDs |
| Tolerance matching | Amounts or dates agree within a limit you set | Rounding and settlement-day differences |
| Reference-free matching | Customer, date and amount are compared when there's no usable reference | Bank transfers with vague memos |
| AI investigation | The AI checks settlement reports, fee lines, refunds and references to explain a difference | Everything the rules couldn't match |
One-to-many matching depends on payout detail. Stripe's payout reconciliation report, for example, lists the transactions settled in each payout, which is what lets one deposit be matched to hundreds of orders. Greenloom doesn't force a match on a likely guess. If the evidence doesn't support a pairing, the transaction stays unmatched and goes to a person, with everything the AI found attached.
What are the key features of transaction matching software?
The key features of transaction matching software are data integration, a matching rules engine, complex matching, anomaly detection, exception handling and reporting. Here's how Greenloom covers each one.
Multi-source data ingestion
Bank, Stripe, PayPal, Shopify and Amazon data comes in alongside your ERP records, or as uploaded statements on day one.
Data standardization
Dates, amounts, currencies, references and transaction IDs are put into one format before anything is compared.
A matching rules engine
Configured rules match on the fields you choose, with tolerances for rounding and settlement timing.
Complex matching
One-to-many, many-to-one and many-to-many matches, so bundled payouts and split payments pair correctly.
Matching without a reference
When a payment has no usable reference, Greenloom compares customer, date and amount, and only matches when the evidence supports it.
AI investigation of unmatched items
Every transaction the rules can't match is checked against settlement reports, fee lines, refunds and references.
Anomaly detection
Duplicate references, late and split payouts, and fees above your agreed rate are held with a plain-English reason.
Exception handling
Only unexplained items reach a person, and each one has an owner, an age and its evidence attached.
Approval before posting
Matches and proposed entries wait for a named person to approve them before they reach your ERP.
Match reports and audit trail
See what matched, by which rule or evidence, what's still open, and who approved each decision.
What doesn't Greenloom do?
You should know where any matching tool stops. Here's where Greenloom stops.
- It doesn't match supplier invoices to payments for accounts payable, or reconcile payroll and intercompany transactions.
- It doesn't force a match on a likely guess. Without evidence, a transaction stays unmatched for a person.
- It doesn't post anything to your ERP without approval.
- It doesn't replace your ERP or general ledger. It works alongside them.
How does automated transaction matching work?
Automated transaction matching works in six steps. The software collects data from each source, standardizes it, runs the matching rules, investigates what's left, explains or flags each difference, and records your team's approval.
Collect the data
Bank lines, payout and settlement reports, and ERP entries for the same period come in from each source.
Standardize it
Every record is put into the same structure so a date, amount or reference means the same thing on both sides.
Run the matching rules
Exact and tolerance rules pair one-to-one, one-to-many, many-to-one and many-to-many matches.
Investigate the leftovers
The AI examines each unmatched transaction against the evidence available, such as fee lines, refund IDs and payer names.
Explain it or flag it
If the evidence supports a reason, the difference is explained and recorded. If not, it becomes an exception.
Approve and post
Your team reviews the short exception list and approves matches and entries, which then post to your ERP.
Should you trust rules-based or AI-based transaction matching?
Use both, in that order. Rules-based matching is predictable and repeatable, which is what auditors want for the clear matches. AI-based matching is better at the unclear ones, like a deposit with a vague memo or a payout net of fees.
The risk with AI matching is a confident match that's wrong. That's why Greenloom's AI works under three conditions:
- It shows the evidence behind every match and explanation.
- It leaves a transaction unmatched when the evidence isn't there.
- It never posts to your ledger without a person's approval.
What does automated transaction matching look like in practice?
Automated transaction matching turns a bundled payout, a reference-free payment or a set of partial payments into clear matches with evidence. Choose an example to see one.
| Line | Amount | Evidence | Result |
|---|---|---|---|
| 142 orders recorded in the ERP | $10,214.00 | Order IDs in the payout report | Matched |
| Processing fees | −$297.80 | Fee lines in the payout report | Explained |
| Refund on one order | −$70.00 | Refund line with the order ID | Explained |
| Payout received | $9,846.20 | Bank statement | Fully matched |
What your team sees
One deposit linked to 142 orders, with fees and a refund shown as their own lines.
One-to-one matching would have found nothing, because no order equals $9,846.20.
| Check | Value | Evidence | Result |
|---|---|---|---|
| Bank deposit, memo "PAYMENT THANK YOU" | $4,250.00 | No invoice number | No reference |
| Customer | Payer name matches Northwind Supply | Evidence found | |
| Amount | $4,250.00 | Equals open invoice INV-2207 | Evidence found |
| Date | Two days after the invoice due date | Evidence found | |
| Proposed match | $4,250.00 | Deposit to INV-2207 | Waiting for approval |
What your team sees
A proposed match with all three pieces of evidence, ready to approve.
If two open invoices had the same amount, Greenloom would hold the deposit as an exception rather than guess.
| Line | Amount | Evidence | Result |
|---|---|---|---|
| ERP | |||
| Invoice INV-3101 | $6,000.00 | Open invoice, same customer | Matched |
| Invoice INV-3102 | $4,000.00 | Open invoice, same customer | Matched |
| Bank | |||
| Transfer, March 3 | $3,500.00 | Same payer | Matched |
| Transfer, March 10 | $3,500.00 | Same payer | Matched |
| Transfer, March 17 | $3,000.00 | Same payer | Matched |
| Both sides | $10,000.00 | Totals agree | Fully matched |
What your team sees
Three transfers and two invoices closed as one group, with the payer and totals as evidence.
Until the third transfer arrived, the first two were held as partial payments with a reason.
See your own transactions matched
Bring one month of bank and payout statements. In a 30-minute call, we'll match it live and show you every match, explanation and exception.
What is a good transaction auto-match rate?
A good auto-match rate depends on your data, so a vendor's headline number only means something if you know how it was measured. The better test is how many transactions still need a person after matching and investigation, measured on your own data.
| Question to ask | Why it matters |
|---|---|
| Is the rate by number of transactions or by value? | Thousands of small, clean matches can hide a few large problems. |
| Does it count suggested matches that a person still has to check? | A suggestion isn't a match until someone approves it. |
| How many matches turned out to be wrong? | A high rate with false matches is worse than a lower rate without them. |
| What data was it measured on? | Clean card data matches easily. Bank transfers with vague memos don't. |
| What happens to the transactions that didn't match? | This is where your team's hours actually go. |
Measure it on a month you've already closed
We'll run Greenloom on that month, so you can compare the results with what your team already knows to be right.
What can you use transaction matching software for?
You can use transaction matching software anywhere two systems record the same money. Greenloom focuses on the matches where money comes in: bank, card processor, marketplace and customer payments.
| Use case | What gets matched | Greenloom |
|---|---|---|
| GL-to-bank | Bank statement lines to ledger entries, including timing differences | Covered |
| Card processor settlements | Stripe and PayPal payouts to charges, refunds, disputes and fees | Covered |
| Marketplace and e-commerce settlements | Amazon settlements and Shopify payouts to orders and fees | Covered |
| Customer payments to invoices | Transfers, including short, partial and reference-free payments, to open invoices | Covered |
| Delivery app and POS payouts | Payouts to sales, with commissions shown separately | Covered |
| AP invoice to payment | Supplier invoices to outgoing payments | Not covered |
| Payroll and intercompany | Payroll runs and group transactions to the GL | Not covered |
Who is automated transaction matching software for?
Businesses with high transaction volumes
E-commerce brands, restaurants and consumer goods companies with thousands of payments a month across several channels.
Finance teams with complex matches
Teams dealing with bundled payouts, short payments, split deposits and payments without references.
Teams that need accuracy and an audit trail
Controllers who need every match explained and approved before the close is signed off.
Why do finance teams choose Greenloom for transaction matching?
Finance teams choose Greenloom because matching doesn't stop at the rules, every match shows why it was made, and people approve what posts.
Matching doesn't stop at the rules
Most of the work is in what rules can't match. Greenloom's AI investigates those transactions and explains each difference it can prove.
Every match shows why
Each match records the rule or evidence behind it, so your team and your auditor can check it in seconds.
People approve what posts
Your team reviews matches and proposed entries before anything reaches your ERP, and every approval is logged.
Is automated transaction matching audit-ready?
Automated transaction matching is audit-ready when every match records the rule or evidence behind it, a named person approves what posts, and your auditor can trace any match back to the source records.
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 a number is reliable. For matching, that means auditors want to see why two records were paired, not just that they were.
Questions to ask any transaction matching vendor
- Can we see the rule or evidence behind every match?
- Can the software post to our ledger without approval?
- Where is our data stored, how is it encrypted, and is it used to train models?
- Can our auditor get read-only access to the match history?
We answer each of these in writing on the first call.
What the match log looks like
- 4,812 matched by rule: reference and amountMatched
- Payout matched to 286 ordersMatched
- Explained: $297.80 fees from payout reportExplained
- Held: duplicate payout referenceException
- Approved by the accounting managerApproved
How does Greenloom compare with other transaction matching tools?
Transaction matching tools fall into four types. Each one puts the work in a different place, so the right choice depends on what your unmatched transactions look like.
| Type | Where the work sits | Best fit when |
|---|---|---|
| Spreadsheets with lookup formulas | People pull data, write lookups and investigate by hand | You have low volumes and clean references |
| ERP bank matching (NetSuite, QuickBooks, Xero, Sage Intacct, SAP, Oracle, Microsoft Dynamics) | Matches bank lines to items already in the ledger | Payments carry clear references and arrive gross |
| Enterprise matching and close platforms | High-volume rules-based matching inside a wider close and certification workflow | You need matching across many reconciliation types and entities |
| AI reconciliation layer, such as Greenloom | Runs alongside your ERP, matches incoming money and investigates what's left over | Bundled payouts, short payments and missing references fill your unmatched list |
Which Excel functions can you use for transaction matching?
In Excel, teams usually match transactions with XLOOKUP or INDEX and MATCH to find a reference on the other side, COUNTIFS to spot duplicates, and SUMIFS to compare totals. These work for one-to-one matches with clean references. They break down with one-to-many payouts, partial payments and vague memos, which is usually when teams move to software.
For the bigger picture, read our guide to financial reconciliation.
Bring three test cases to every demo
- A payout that covers hundreds of orders
- A bank deposit with a memo that means nothing
- A customer who paid two invoices in three transfers
Watch for these red flags
- Match rates quoted without saying how they're measured
- Matches with no rule or evidence shown
- Automatic posting to your ledger
- Demos that only use the vendor's sample data
What are the benefits of automated transaction matching?
Automated transaction matching saves hours of line-by-line work, makes your books more accurate, and leaves your team with a short list of real exceptions instead of a long list of unmatched lines.
Hours back every week
Thousands of transactions are matched daily without anyone checking them line by line.
More accurate financial reporting
Fewer manual steps means fewer keying errors and fewer misstatements to correct later.
A shorter exception list
The AI explains what it can prove, so only real exceptions reach your team.
A faster month-end close
Matching runs every day, so month end starts with most transactions already matched.
Errors caught early
Duplicates and fees above your agreed rate stand out as soon as they happen.
Growth without new hires
Add channels and volume without adding people to match them.
How much time could automated transaction matching save?
Move the sliders to match your month. The calculator estimates how many hours your team spends on 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 investigates these transactions first and sends your team only the ones the evidence can't explain.
Prefer to see real numbers? Match one month of your data live.
How long does it take to set up transaction matching software?
With Greenloom, you see your own transactions matched during the first 30-minute call, and you can run a full close in parallel before you pay anything.
- Day 1
Match a month live
In a 30-minute call, we load one month of your bank and payout statements and walk you through every match 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 matches with your current process and review any differences.
- After that
Go live source by source
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.