TECHNICAL FIELD

The subject matter disclosed herein generally relates to the technical field of special-purpose machines programmed with a machine-learned model that facilitate adding new features to a reconciliation.

BACKGROUND

The present subject matter seeks to address technical problems existing in conventional payment processors. For example, while payment processors seek to provide merchants with an easy and efficient mechanism for collecting payments from customers, collecting such payments may encounter difficulties for various reasons. One such difficulty involves payments that are initiated from the payer-side with no need for interaction from the payee-side. Such payments are often called “Push” payments, as the payer is essentially “pushing” the payment to the payee. Examples of push payments are wire transfers, Automated Clearing House (ACH) transfers, and direct deposits.

One technical problem encountered when push payments are sent to companies or other large entities that have many potential payees is reconciliation. Specifically, it can be challenging to match up these push payments with specific invoices or payers. Typically, there is a “memo” field available for payers to identify themselves and/or invoice numbers of the invoices they are paying, but this, and other similar fields, are often misused or simply unused by payers, resulting in a time-consuming and difficult process for the payee to match up particular push payments with particular invoices.

