How to use AI to reconcile international payments in Excel

It’s month-end, and a batch of payment cases in your workbook don’t line up. One involves two currencies. Another has a separate fee. You also have a split remittance, two similar beneficiary names, a payment across two periods, and a rejected or returned movement.

AI can help you clean the exports, compare the records, and investigate the rows that still don’t match. But which results can you prove? And which are only candidates for review?

The key is to divide the work well. Let repeatable rules clear exact matches. Use AI to prepare the data and investigate the exceptions. Keep the final decision with the people who understand the accounting context.

The 7-step method in this guide will help you:

• Define the evidence and scope for the run
• Build a workbook that preserves the source records
• Separate exact matches from exceptions
• Use AI to investigate the remaining questions
• Close with control totals and authorised review

First, let’s look at where AI adds the most value.

Why AI should investigate exceptions rather than approve matches

AI is most useful on either side of exact matching.

• Before matching, it can map fields, clean formats, and organise records
• After matching, it can group exceptions, explain differences, and suggest what to check next
• In between, formulas or Power Query can clear the records that meet your exact rules

This division gives you the speed of AI without making the result hard to explain. Exact matches follow a rule you can run again. Exceptions arrive with the source records and differences a reviewer needs to make a decision.

Why draw the line there?

A plausible answer isn’t proof. The NIST Generative AI Profile calls for evaluation against known ground truth, provenance, human oversight, and monitoring. Microsoft likewise recommends that users review and verify Copilot data insights in Excel.

Microsoft’s Financial Reconciliation agent follows a similar pattern. It sorts rows into matched, potentially matched, and unmatched groups while retaining source IDs. Its responsible AI guidance also makes clear that classifications need review.

Each source still answers a different question. An iBanFirst movement shows what our platform recorded on the payment side, while a bank statement shows the external posting. An invoice or remittance provides commercial context, and the ledger shows how finance recorded the transaction. Because those records have separate jobs, none automatically authorises a payment or signs off the reconciliation.

Our broader payment reconciliation guide explains the general process. Here, we’ll stay with the narrower Excel and AI job at hand, starting with one way to bring useful payment data into the workbook.

How the iBanFirst Claude MCP for Excel brings account data into Excel

The iBanFirst Claude MCP for Excel lets you bring permitted iBanFirst account data into Excel through Claude. You can ask for the information you need and organise it into workbook-ready tables, totals, and summaries. That includes:

• Account balances and details
• Financial movements, payment details, and statuses
• FX trades and forward payment contract information

This gives you a clearer starting point for comparing payment records, tracing differences, and preparing exceptions for review. You can then add bank statements and ledger records from your approved sources to complete the source set for the run.

The seven-step method below shows you how to structure the workbook and work through those records.

How to reconcile international payments in Excel with AI (in 7 steps)

A controlled run moves in a deliberate order.

First define the job and preserve the source records. Then bring in the approved data, standardise it, clear exact matches, investigate exceptions, and check the final totals.

The order matters.

Matching before scope is clear gives the result no stable meaning. Sending every row to AI creates more ambiguity than necessary. Closing before the totals reconcile can leave missing records invisible.

The hypothetical cases we mentioned in the intro will move through the steps as one population. Each step should leave the workbook in a state that the next step can use and another authorised reviewer can reproduce.

Let’s start before the import actually happens.

Step 1: Define the reconciliation

Before you import anything, create a short scope sheet that tells everyone what the run covers and how results will be decided.

In your scope sheet, record:

• The legal entity, account, period, cut-off date, and expected population
• Each source you will use and what it can establish, including the iBanFirst movement, external bank posting, invoice or remittance, and ledger
• The approved IDs, dates, amounts, currencies, tolerances, fee treatment, and duplicate rules used for matching
• The outcomes your team permits, including exact match, candidate relationship, unresolved exception, reviewed decision, and approved exclusion
• Who on your team prepares, reviews, owns each accounting judgement, and signs off

Keep any AI confidence score separate from the permitted outcomes. It can help a reviewer prioritise work, but it does not set the reconciliation status.

You’re ready to proceed when a reviewer can state the population, the rule behind each status, and the owner of every remaining decision.

Step 2: Set up the workbook

Your workbook should keep the source evidence separate from every transformation, result, exception, total, and run record.

Organise it into six adaptable areas, using sheets, tables, or another clear structure that suits your process:

• Raw data: The unchanged records from each source
• Prepared data: Cleaned fields that can be compared
• Match results: Exact matches and the rule used for each one
• Exceptions: Rows that need more work and their review status
• Control totals: Record counts and amounts at each stage
• Run log: Dates, rule versions, owners, changes, and outcomes

Keep the original source ID on every row. When you add cleaned names, dates, or other prepared fields, place them beside the original values instead of replacing them.

You can use Show Changes in Excel to trace edits, while file, workbook, and worksheet protection can reduce accidental changes to the raw data area.

You’re ready for data when you can select any result and trace it through each transformation to every original source row.

Step 3: Bring the source data into Excel

Bring in only the records listed in your scope sheet. For each source, record:

• The source and retrieval time
• The period and company requested
• The number of records returned
• Any missing data, empty results, or errors

The data may arrive through an export, Power Query, the iBanFirst Claude MCP for Excel, or an API connection. Label each route so you know where every row came from and what that source can establish.

If you use the iBanFirst Claude MCP for Excel, financial movement history covers the previous 12 months, and each token covers one company. For a multi-entity reconciliation, retrieve and label each company separately.

The key is this: Only bring in the fields the run needs.

This makes the workbook easier to manage and gives AI a smaller, clearer data set to work with. Record who owns the privacy, permission, and retention decisions for each route. The ICO’s data minimisation guidance for AI and Power Query privacy levels can help inform those controls.

Before moving on, compare the returned record count with the number you expected. Resolve any gaps before you start changing or matching the data.

Step 4: Standardise the records

Work from copies of your approved source records, and leave the raw evidence unchanged.

Align your data types, IDs, payment types, signs, and approved date fields. Apply consistent, approved rules for spaces and letter case. Keep the original amount and currency in separate fields. If you convert an amount, record the approved exchange-rate source and date, plus the policy owner.

Power Query lets you record each preparation step, merge tables on same-type columns, and refresh supported connections without rebuilding the process each month.

Use AI for a small set of preparation jobs:

• Map fields between two exports
• Find unusual formats or missing values
• Suggest cleaning rules for review
• Explain why a Power Query step failed

Review each suggestion, then store the task, proposed change, reviewer decision, and final approved rule.

You’re ready to match when rerunning the transformation against the same source snapshot produces the same prepared rows and makes each added assumption visible.

Step 5: Clear exact matches with repeatable rules

Start with the strongest identifier approved in your scope sheet. Assign an exact match only when the identifier is unique and the required entity, account, amount, native currency, sign, status, and date fields agree.

Excel can support the pass in three ways:

• XLOOKUP uses exact mode by default but returns the first result, so check that your key is unique
• Power Query Merge can compare same-type columns and use anti-joins to expose unmatched rows
• SUMIFS can total native-currency amounts, while COUNTIFS or equivalent controlled counts can compare your population counts when ranges and filters are explicit

In the hypothetical population, a row with a unique approved identifier may clear when every required field agrees. The fee stays separate, while the split, similar-name, cross-period, and returned or rejected records remain exceptions.

Store the rule version and source-row IDs with each exact result, then rerun the rule. Once your rerun reproduces the exact match population and exposes every unmatched row, pass those failed rows and reasons to Step 6.

Step 6: Use AI to investigate the unresolved population

Give AI your failed rows and the minimum approved context from the scope sheet. Keep proved matches out so your request stays focused.

Ask it to:

• Group rows that could form a split remittance
• Explain which fields differ and why the exact rule failed
• Identify the evidence or next check a reviewer needs
• Draft a fact-based exception note for correction

Require candidate source-row IDs, differences, missing evidence, next check, and a non-authoritative candidate status. Preserve the task, output, and reviewer decision.

For similar beneficiary names, Fuzzy Merge or AI can surface likely rows, but approved evidence must establish identity.

When you investigate an approved SWIFT payment with a status exception, international payment tracking can add payment-side status and journey evidence without standing in for the bank record or ledger.

Your exception is ready for review when it points to retained source rows, explains why the exact rule failed, and names one next action. You can then record the decision and pass the population to Step 7.

Step 7: Check completeness and obtain authorised sign-off

Before sign-off, reconcile your population:

• Compare record counts across intake, prepared data, exact matches, candidates, exceptions, approved exclusions, and carried-forward items
• Compare native-currency totals by the dimensions in your scope sheet and keep unlike currencies separate
• Confirm every source row has exactly one accounted-for state in your run
• In your run log, record the source scope, retrieval time, rule versions, preparer, reviewer, evidence inspected, changes, open exceptions, and outcome
• Give every open item an owner and status, then route the run for authorised sign-off

If a row appears twice or not at all, return to the last stage where counts and totals agreed.

You can close when the accounted-for population equals the approved source population, the totals agree, every exception has an owner and status, and authorised sign-off is recorded. Then ask whether the Excel process can keep the loop controlled and maintainable next month.

Where Excel fits, and when a dedicated payment provider may be the better approach

The decision turns on control and maintainability, not a universal row or transaction threshold. Excel remains a good fit when:

• You use a bounded population and a small number of known sources
• Power Query or formulas prepare the data in a repeatable way
• Exceptions stay visible and easy to review
• One controlled workbook can preserve the source trail
• Your team completes the run without hidden manual steps

A dedicated payment provider becomes more useful when you see:

• Repeated manual exports and a growing number of sources
• Fragile formulas, conflicting copies, or concurrent editing problems
• Stale refreshes, weak source trails, or growing exception queues
• Permission conflicts or blurred legal-entity separation
• Sign-off evidence assembled outside the run

One issue may be easy to repair. A pattern of them means the workbook is creating more work and control risk than it removes.

Moving to a connected setup doesn’t mean replacing your accounting system. At iBanFirst, our integrations and automation routes can connect payment data with accounting software, internal tools, and other financial systems.

Keep Excel while your team can demonstrate the full seven-step loop consistently. Consider a connected route when maintaining the workbook becomes a monthly problem of its own. Once you know which data and system job you need, you can assess where iBanFirst fits.

How iBanFirst supports your reconciliation workflow

International payment reconciliation gets harder when payment data, status checks, and accounting records move through separate manual routes. That’s where we can help. With iBanFirst, you can:

• Reconcile payments, fees, and transactions through structured payment records and connectivity
• Check approved SWIFT payment status when an exception needs payment-side evidence
• Connect payment and accounting infrastructure when recurring workbook stitching becomes harder to maintain

Your finance team keeps authority over matching, accounting, payment approval, and sign-off while our platform supplies the payment-side records and connections your workflow needs.

If iBanFirst fits your process, request an account to explore how we can support your finance team’s reconciliation workflow.

More questions about AI-assisted payment reconciliation in Excel

These questions cover three practical points that remain after the method, including match authority, data scope, and access to records outside iBanFirst.

Can AI automatically clear payment matches in Excel?

No. An AI suggestion, score, classification, or explanation shouldn’t clear a payment match on its own.

Automation can assign an exact match status when your approved, repeatable rule proves the required field agreement, key uniqueness, source-row lineage, and population conditions. That result comes from the rule, while AI can help organise the rows that fail it. Keep candidate relationships and unresolved exceptions visible until an authorised reviewer records the decision.

What data should you give AI for payment reconciliation?

Give AI the minimum approved records and fields needed for one defined preparation or investigation job.

Useful fields may include the source-row ID, entity and account, approved dates, native amount and currency, reference, payment status or type, and the reason the exact rule failed. Keep the raw evidence outside the AI output, and exclude unrelated personal or commercially sensitive data. Your privacy or security owner should decide the permitted connector, retention, and jurisdiction setup.

Can the iBanFirst Claude MCP for Excel access external bank statements or your ledger?

No. The iBanFirst Claude MCP for Excel retrieves permitted iBanFirst account data and prepares workbook-ready material. A separate Excel MCP may write that material to the workbook.

External bank statements, invoices, remittance evidence, and ledger records must enter through separately approved routes controlled by your finance team. Each route keeps its own permissions and source authority. The practical rule is simple. A connector’s access does not enlarge what its source record can prove.