Payment and marketplace reconciliation automation at scale

The service breaks every payout deposit down into its underlying transactions, fees, refunds and reserve movements, matches those pieces to the corresponding sales, chargebacks and adjustments in your system, and consolidates every processor and channel into one reconciliation view rather than a separate file per source. Anything the automated matching cannot resolve with confidence lands in a defined exception queue with the specific transaction data attached, not just a flag saying something is wrong. Reserve holds that release weeks after the original payout are tracked as their own line item and matched when they release, rather than treated as an unexplained deposit with no visible history behind it.

We start by mapping every payment channel currently in use, Stripe, a marketplace, Adyen, a second processor, and how each one structures its payout reports, since fee and refund treatment differs meaningfully between them. Automated matching then runs against your sales, refund and chargeback records on a daily or weekly cadence depending on volume, pairing each payout component to its source transaction. Anything that cannot be matched with an acceptable confidence level, a payout that does not tie to the expected total, a refund with no matching original sale, an unfamiliar fee type, routes to the exception queue rather than getting forced to balance. A person works that queue on the agreed cadence, and every resolution, along with the reasoning behind it, gets logged against that specific exception.

This is built for businesses processing a genuinely high volume of payment transactions, typically thousands a day across one or more channels, where manual reconciliation has either already broken down or is close to it. Marketplace sellers running Amazon, Shopify and a direct storefront simultaneously, fintech and payments businesses processing on behalf of their own customers, and subscription businesses with high transaction counts on Stripe are the most common fits. A lower-volume business processing a few dozen transactions a day is typically better served by standard reconciliation inside monthly bookkeeping or basic bank and card reconciliation, where the volume does not yet justify a dedicated automation layer.

Lump-sum payouts that used to get booked as one number, hiding the sales, fees, refunds and reserves inside them, now get broken apart and matched at the line level. Discrepancies that used to look like three separate unexplained variances across three processor files now show up as a single exception in one consolidated view. Reserve releases that used to land weeks later and get booked as a mystery deposit now match automatically back to the original hold. And the backlog problem, where reconciliation for a high-volume channel falls a month or two behind because nobody has time to work through it by hand, resolves because the exception queue stays small enough on a daily or weekly cadence to actually work through, instead of growing every week it goes untouched.

Stripe and Adyen are the most common processors on the payments and SaaS side, each exposing payout, fee and refund data through their own reporting APIs at a level of detail well beyond the single lump-sum figure a bank statement shows. For marketplace sellers, this builds on A2X and Link My Books integration, which already breaks Amazon and Shopify payouts into sales, fees and refunds before this layer adds cross-channel consolidation on top. The reconciled output lands in QuickBooks Online, Xero or NetSuite depending on your existing setup, so the consolidated view is a working reconciliation feeding your ledger, not a side report that duplicates it.

Scope and pricing here are set by transaction volume, since a business processing two thousand transactions a day across three channels is a different job from one processing two hundred across a single processor. This is typically scoped alongside a Growth or Scale engagement on our pricing page, and the actual number comes from a scoping call that looks at real transaction counts and channel complexity rather than a flat listed rate.

The exception rate, the share of transactions that need a person versus the share that match automatically, is the core metric, tracked by channel so a specific processor's matching quality is visible on its own rather than blended into an overall number that hides a weak spot. We also track how quickly the exception queue gets worked through relative to its target cadence, since a queue that grows faster than it gets resolved defeats the purpose regardless of how good the automated matching itself is. Both numbers get reviewed with you on a set cadence, typically monthly, alongside your regular close review.

Every match and every exception resolution is logged with what it matched to, the reasoning, and who resolved it if a person was involved. That trail is what a fintech or payments business needs when an investor or a regulator asks how a specific payout reconciles, since the answer needs to be demonstrable from a record, not reconstructed after the fact from memory and a spreadsheet. It is also what makes a later audit or diligence process faster, because audit-ready books depend on exactly this kind of transaction-level trail existing before anyone asks for it.