Korvena Recon · AI-enabled

Reconciliation that closes itself, and proves it

Replace spreadsheet reconciliation with an engine that ingests every source, matches at scale with rules, fuzzy logic and AI, routes exceptions to owners, and leaves an audit trail your examiners will thank you for.

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The problem

Every settlement day, the same fire drill

Core banking says one thing. The processor file says another. The switch report arrives late, the bank statement uses different references, and someone on the operations team owns a spreadsheet that everyone fears. Unresolved breaks become write-offs, audit findings and regulatory questions.

Manual matching doesn’t scale

Transaction volumes grow faster than operations headcount. Every new channel or partner adds another file format to the pile.

Breaks age in the dark

Without ownership and ageing, exceptions sit unresolved until they surface as customer complaints or audit findings.

Evidence is scattered

When a regulator or auditor asks how a figure was reconciled, the answer should not live in one analyst’s inbox.

Capabilities

What Korvena Recon does

AI in Recon

Matching that learns from your team

Deterministic rules settle the clear cases. Machine learning handles the rest, and every suggestion stays subject to the same approvals as a manual match.

Learned match suggestions

Exceptions the rule cascade cannot settle receive ranked candidate matches, learned from the matches your analysts confirm and reject.

Break anomaly alerts

Unusual break volumes, ageing or patterns by source, channel or counterparty are flagged early, before they become write-offs.

Explained and approved

Each suggestion shows the fields and evidence behind it. Nothing posts without maker-checker approval, and every decision is logged.

How it works

From files to closed books in four steps

Ingest

Connect sources by SFTP, API or upload. Formats are normalised into a consistent transaction model on arrival.

Match

The engine applies your rule cascade (exact first, then tolerance and fuzzy passes), then AI models propose ranked matches for whatever remains. The basis for every match is recorded.

Resolve

Exceptions route to owning teams with categories, ageing and escalation. Adjustments go through maker-checker.

Report

Positions, break ageing and resolution metrics are always current, for operations, finance, auditors and regulators.

Where it fits

Typical reconciliations institutions run on Korvena Recon

ReconciliationSources
Card & switch settlementCore banking vs processor files vs switch reports vs settlement bank statements
Agency banking & walletsWallet ledger vs agent float accounts vs partner bank statements
Transfers & payoutsInternal ledger vs payment-gateway reports vs bank statements
Nostro / correspondentInternal nostro ledger vs correspondent bank statements
Loan disbursement & collectionLoan management system vs disbursement channel vs repayment collections

Bring one messy reconciliation to the demo

The fastest way to evaluate Recon is to see your own worst reconciliation modelled in it. We will show you exactly how ingestion, matching and exception flow would work.

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