Anomaly Detection in Bank Reconciliation: AI That Catches Unmatched and Unusual Items

A company selling through a dealer and current account network sees dozens of bank movements a day, and hundreds during busy periods. Transfers, EFTs, POS collections, refunds, fee deductions. Most of these are automatically offset against the correct current account based on reference and amount. The problem arises because this automatic flow is not one hundred percent accurate. If a reference is entered incompletely the transfer stays unmatched, if two people process the same receipt a duplicate entry forms, if two current accounts with similar names get mixed up the payment goes to the wrong account.

If these small errors are not caught day by day, they pile up. Demir Ticaret's 8.000 TL payment recorded twice shows more credit on the current account statement than it should, and the dealer rightly objects. A transfer that lands on the wrong current account makes a dealer who has already paid look like a debtor and triggers unnecessary collection pressure. An EFT left unmatched widens the gap between the bank balance and the accounting record.

Searching for these items at period-end through manual reconciliation is time-consuming and wearing. Scanning one by one through hundreds of lines is both slow and, combined with fatigue, breeds new errors. This is exactly where the purpose of reconciliation anomaly detection becomes clear: noticing these items around the day they occur, without waiting for month-end, and bringing them in front of you.

The process is built on top of the existing B2BPro flow and requires no separate connection or new bank authorization. In the first step, B2BPro reads your bank account activity and automatic offset records in read-only mode, just as it does today. Anomaly detection listens to this ready data flow and writes nothing into the system from the outside.

In the second step, the AI compares incoming items against your past matching habits. Which amount a dealer usually pays, with which reference, how often; which current account comes from which bank account. It flags items that fall outside these patterns: suspected duplicate entry, unexpected amount, unmatched transfer, unusual current account match. Next to each flag comes a plain explanation of why it drew attention, so you meet a clear note rather than a technical alert.

The third step is entirely yours. You review the flagged items on a single screen, read the assistant's note, and make the decision. If you want to correct it you go to the relevant record and step in, and if the item is actually fine you close the flag by saying 'no problem.' No step proceeds without your approval, and a record stays of which flag was resolved by whom and how.

The first group is unmatched items. Transfers that could not be linked to a current account in the automatic offset, collections left open, and movements with no counterpart found on the bank statement fall into this group. For example, if three transfers that came in yesterday have not yet matched any current account, the assistant gathers them as an open-item list; instead of searching one by one, you see them all together.

The second group is duplicate entries and unexpected amounts. Cases where the same amount and the same receipt number appear to have been processed as two separate collections are flagged as a suspected duplicate. Alongside this, when a dealer who pays the same amount every month sends a markedly different amount this time, an alert comes. If Yıldız Toptan usually pays around 5.000 TL but this time 47.500 TL came in, this is either a bulk payment or an error; in either case it is worth a look.

The third group is unusual matches. A transfer that always lands on the same current account with the same reference appearing this time to be offset against a different current account is a typical example. These items usually go quietly to the wrong account and can stay that way for months unnoticed. The assistant flags these deviations by comparing them against the past pattern.

Say 320 bank movements came in during the month and 300 of them were offset without issue. The assistant flags the remaining 20 items as suspicious and puts them in front of you. At the top of the list is a 12.480 TL transfer belonging to Demir Bayilik; although it came in with its usual reference, this time it appears to have matched a different current account. With one click you move it to the correct current account and solve the problem mid-period.

Further down the list is a collection flagged as a suspected duplicate. The same amount and the same receipt number were entered twice; most likely two different people processed the same payment. You reverse one and correct the inflation on the current account statement. Had this item not been caught, the dealer would have claimed extra credit on its next payment.

At the end of the list are a few unmatched transfers and a movement with no counterpart on the statement. Some of these you match manually because of a missing reference, and some you find to be false alarms and close by saying 'no problem.' After a twenty-minute review, the gap between your bank balance and your accounting record has moved close to zero.

The B2BPro bank integration only listens to movements; it has no authority to send money, make payments, or perform transactions on the account. Reconciliation anomaly detection stays within the same frame. The AI works on this read-only data, changes no record, does not break the offset, does not close a collection, and makes no money movement. All it does is place a flag and suggest an explanation.

The privacy of your data is a principle designed in from the start. Your bank and current account data belong to you and are not used to train models shared with other companies. The feature is being developed in line with the principles of Law No. 6698 on the Protection of Personal Data. The assistant reviews only your own data, solely for flagging and explanation.

These limits are a deliberate choice. In financial records, an autonomous system making corrections on its own can create a chain of new errors instead of solving one. Keeping the decision with the human both keeps control in your hands and ensures that every correction stays traceable and reversible.

It helps to be clear: reconciliation anomaly detection is not a usable feature right now. It is being developed in an early-access phase and will be rolled out gradually. This blog post was written to honestly explain in advance how the feature will work once it ships; it does not mean a button you can switch on today.

The frame we offer for the pre-launch period is clear. If you become a B2BPro customer today, reconciliation anomaly detection joins your plan with early and pre-launch usage rights when it is released; it comes as a limited free usage tier at no extra charge. Pre-launch customers keep this right.

In the meantime, you can start using the existing B2BPro infrastructure today, which listens to your bank movements read-only and automatically offsets incoming payments. When the anomaly layer ships, it sits on top of a data flow that already runs clean. Request a free demo and let us review your current reconciliation process together.