Accounts Receivable

Accounts receivable automation software for cash application that closes itself

AR Automation takes the bank statement, the remittance advice and the open invoice ledger and works out which payment belongs to which invoice, even when the customer paid three invoices short of one, rounded the amount and quoted the wrong reference. Matches it is sure of are applied. The rest reach your team as a short exception queue with the evidence lined up.

The invoice-to-cash loopLoop diagram of invoice issued, payment received, remittance read, receipt matched, exception reviewed and balance updated Invoice issuedPaymentreceivedRemittance readReceipt matchedExceptionreviewedBalance updatedContinuousimprovement

What AR Automation is and who it is for

AR Automation is the receivables half of our finance automation family, built for finance teams that reconcile large volumes of customer payments and need to know, every morning, exactly what is still owed. Its data model is built for B2B realities: prepaid, credit and tenant customer types each with their own terms, stored in a PostgreSQL schema that keeps every receipt, every match decision and every reviewer action as a record you can audit later.

The heart of the product is a cash-application matching engine with a three-tier confidence cascade. Exact matches on reference and amount are applied automatically. Fuzzy matches on partial amounts, combined payments and near-miss references are proposed with a score. AI-assisted matching takes the hard remainder, reading remittance advices that arrive as PDFs, spreadsheets and emails to find the invoices a payment was meant to cover. Whatever is left is for a human, and that queue is short.

It is sold as a subscription on transaction volume, with the Business Central connector included and other ERPs connected during implementation. We implement for clients in India, UAE and Saudi Arabia from Chennai and Abu Dhabi, and for US clients from New Jersey, with payment data kept in-region. The upstream order and invoice flow is handled by AI Order Automation, and the matching approach is shared with AP Automation on the payables side.

Three-tier matching cascadeFunnel diagram showing receipts passing through exact, fuzzy and AI-assisted matching tiers to a small human review queue All receipts and remittancesTier 1: exact matchTier 2: fuzzy matchTier 3: AI-assisted matchHuman review queue

What AR Automation does every banking day

Four capabilities. Each receipt moves through them with a live status your team can watch rather than a batch report they wait for.

01

Cash application

Ingests bank statement lines and payment gateway settlements, pairs them with remittance advices from email, PDF and spreadsheet, and applies them to open invoices through the exact, fuzzy and AI-assisted cascade. Short payments, overpayments and combined payments are recognized and split correctly.

02

Exception handling

Receipts the cascade cannot settle with confidence are queued with the candidate invoices, the remittance text and the score side by side. A reviewer accepts, redirects or creates a dispute in a few clicks, and each decision is stored to improve the next match.

03

Receivables visibility

Open items, unapplied cash, disputed amounts and aging by customer are visible in one view that updates as matches are applied. Finance leaders see what is actually outstanding, not what the ledger said at the last batch run.

04

Collections support

Generates reminder sequences by customer type and aging bucket, records promises to pay and supports auto-debit and auto-collection set-ups for customers who have mandated them, so follow-up is consistent and nobody is chased for an invoice that was paid yesterday.

How AR Automation works from payment to balance

Four steps, running continuously as payments land. The reviewer's screen updates as each receipt moves because the services stream to each other rather than hand off files.

  1. 01

    Receive payment data

    Bank statements, gateway settlements and remittance advices are ingested and normalized, with the source document kept for evidence.

  2. 02

    Match to open invoices

    The three-tier cascade runs: exact, then fuzzy, then AI-assisted. Confident matches are applied and posted to the ERP.

  3. 03

    Review exceptions

    The remainder reaches a reviewer with candidates ranked. Decisions are recorded and feed back into matching rules.

  4. 04

    Track outstanding balances

    Aging, unapplied cash and disputes update in real time, and collections sequences adjust to what is really owed.

Product note on scope

AR Automation covers payment matching, exception handling and collections. The upstream sales-order-to-invoice workflow is handled by AI Order Automation, and the two share a data model so an order created there is the invoice matched here. Bank connectivity depends on the statement formats and gateway APIs available in your market; we confirm them in discovery. The product is designed to work alongside e-invoicing regimes in UAE, Saudi Arabia and India, consuming the structured invoice data they produce, and we do not describe it as certified by any of them.

Frequently asked questions

What happens when a customer pays several invoices with one transfer and quotes no reference?

This is the case the cascade is built for. The exact tier fails, the fuzzy tier looks for combinations of open invoices for that customer that sum to the amount within your tolerance, and the AI-assisted tier reads any remittance advice that arrived separately by email. If a unique combination is found above your confidence threshold it is applied; otherwise the reviewer sees the candidates ranked and picks.

How does AR Automation get remittance advices?

The remittance ingestion pipeline watches a shared mailbox and document folders, recognizes remittance advices in PDF, spreadsheet and email-body form, extracts the invoice references and amounts and links them to the bank receipt they explain. Customers do not need to change how they send them. Unreadable or incomplete advices are flagged so your team can ask the customer rather than guess.

Which ERPs and banks does it connect to?

Microsoft Dynamics 365 Business Central is the reference ERP integration, built as an AL extension. Other ERPs are connected through their APIs or file interfaces during implementation; we have also benchmarked the product against Oracle Fusion receivables. Bank statements are ingested in the formats your banks provide, from MT940 and camt.053 to CSV portal exports, and payment gateways through their settlement APIs.

Is AR Automation sold separately from AP Automation and AI Order Automation?

Yes. Each is a separate subscription priced on its own transaction volume, and many clients start with the one that hurts most. Because the three share a data model and the same ERP connectors, adding a second product later is an entitlement change rather than a new implementation. We do not publish list prices because volumes and ERP effort vary; we quote after discovery.

Where does our payment data live?

In the environment we deploy for your tenant: in-region cloud for UAE and Saudi clients, in India for Indian entities, or inside your own cloud account where your bank, regulator or group policy requires it. Receipts, remittances and match history stay there, and the ERP connector moves only posting data. We agree the residency arrangement in writing before the pilot begins.

How quickly can we see results on our own data?

A pilot typically starts with three months of historical bank statements, remittances and the open invoice ledger for one entity. We replay them through the cascade and show you the match rate by tier and the exception queue you would have faced, which is the honest way to judge the product. The live connection to your ERP and banks follows once you are satisfied with those results.

Get in touch

Explore AR Automation with our team

Bring last month's bank statement and the receipts your team struggled to apply. We will walk through the recorded demo, replay your data through the matching cascade and propose a pilot on one entity.