u/Raelynn_Tamarind

I've watched enough fintech finance teams hit the same wall to know the pattern. Volume scales, PSP count grows, and the process that worked at $1M in transaction volume collapses somewhere around Series A. The instinct is to hire more people. The fix is building the process correctly from the start.

Here's how the full workflow should be structured:

Step 1: Connect every data source without an engineering ticket per integration

Every reconciliation process starts with ingestion. The problem is most teams treat this as a one-time setup and then get stuck every time a new payment provider, bank, or processor gets added to the stack.

What you actually need:

API ingestion for payment providers like Stripe, Adyen, and Checkout where real-time feeds are available.

SFTP and file-based feeds for batch settlement files, bank statements, and processor exports that don't expose APIs.

BAI2 and MT940 support for sponsor bank and core banking system feeds. These are the standard formats banks use to deliver transaction and statement data.

No engineering ticket per new integration. If adding a new PSP requires a developer build, the automation stops at the integration boundary. That is not automation. That is outsourced manual work with extra steps.

Most legacy providers like Trintech and HighRadius handle this but come with multi-month implementation timelines. Newer platforms like Rexi, Simetrik, and Ledge are built for faster time to value. The tradeoff between them depends on how complex your data environment is and how fast you need to go live.

Step 2: Standardize everything before a single match is attempted

This is the stage most teams skip and then spend months debugging at the matching layer.

Every source describes the same transaction differently. Not because the data is wrong. Because Stripe, your sponsor bank, your core banking system, and your ERP were never designed to talk to each other. Date formats vary. Currency codes vary. Time zones are not aligned. Counterparty names are inconsistent across systems. Reference identifiers break between PSPs.

Before matching can happen, every record needs to be normalized into one canonical schema:

Date normalization across all sources.

Currency standardization with no ambiguity across codes.

Time zone alignment so the same transaction does not show up on different days depending on which source you are reading.

Counterparty lookup to resolve inconsistent naming.

Transaction type classification so pay-ins, payouts, fees, refunds, and chargebacks are categorized consistently before they hit the matching layer.

If this layer is weak, high exception rates follow. Most teams blame the matching algorithm. The problem is almost always here. Standardization is where the actual reconciliation problem lives and where most tools underinvest.

Step 3: Build matching logic that covers the actual complexity of payment flows

Rules-based matching handles the predictable cases. Amount, posting date, reference identifier, invoice number, with configurable tolerances for the parts that are inherently messy:

Date window tolerance for settlement timing differences between PSPs and banks.

Absolute and relative tolerance for fee rounding and FX differences.

One-to-many and many-to-many matching for net settlements, split payments, and bundled fees. If your tool only handles one-to-one matching it will fail on anything beyond a simple pay-in.

AI-driven matching handles the long tail. Broken reference identifiers, inconsistent description fields, counterparty name variations that rules cannot resolve. This is what gets auto-match rates above 90% on real-world messy data.

The metric to track is auto-match rate. The question to ask any vendor is what happens to the transactions that do not auto-match.

Step 4: Build exception resolution, not just exception flagging

This is where most reconciliation processes fall apart and where most tools stop helping you.

An exception is any transaction that fails to match cleanly and needs investigation. Settlement breaks, duplicate payments, unrecovered processor fees, FX differences, missing chargebacks, refund mismatches. These do not resolve themselves.

A functional exception workflow needs:

Categorization by root cause. Not just a flag. A classification of why the transaction broke.

Routing to the right person. Fee exceptions go to one team. Chargeback breaks go to another.

Transaction-level drill-down. Full context in one view without manually pulling raw files from three different portals.

SLA tracking. So exceptions do not sit in a queue for three weeks until month-end.

From what I've seen inside Rexi's workflow, the exception layer runs categorization, investigation, and audit logging as separate automated steps rather than dumping everything into a queue. Legacy providers like BlackLine and Trintech cover this at enterprise scale but require longer implementation cycles and harder-to-configure logic. Worth understanding the tradeoff before you sign anything.

The gap between "here is your exception list" and "here is why this broke and what needs to happen" is where finance teams either get their time back or do not.

Step 5: Make the audit trail native, not reconstructed

Every match, every exception, every adjustment, every approval needs to be logged at the transaction level. Not because it is best practice. Because SOX, SOC 2 Type II, SOC 1, and sponsor bank reviews require it.

The difference between a clean audit and a painful one is whether the record exists in the system or has to be rebuilt from a shared drive after the fact.

Preparer-reviewer-approver workflows, version history, and full traceability should be built into the process from day one, not retrofitted when the first compliance review arrives. Any platform you evaluate should be SOC 2 Type II certified at minimum before it touches your transaction data.

Step 6: Move from monthly close to continuous reconciliation

When ingestion is real-time and matching is automated, reconciliation stops being a period-end event and becomes a 24/7 background process. Exceptions surface within hours instead of three weeks later when the trail has gone cold. Finance gets a real-time cash position. Treasury gets current liquidity visibility.

Most teams do not get here until steps one through five are solid. But this is the state worth building toward. Reconciliation that runs continuously without a four-day sprint at month-end is what separates a fintech with operational leverage from one that is scaling headcount to keep up with volume.

This is also the foundation for what comes after reconciliation. Broader finance operations automation, accounting, controls, and reporting, without adding headcount every time volume grows.

The evaluation shortcut

When comparing tools, the questions that matter most:

Does it handle multi-source ingestion without an engineering ticket per integration? Does standardization run automatically before matching starts? Does exception resolution include categorization and routing or just flagging? Is the audit trail transaction-level and native? Can the finance team configure matching rules without a developer?

A tool that owns all of this end-to-end is a reconciliation platform. A tool that owns the matching layer is a matching engine. The difference matters a lot at scale.

TL;DR: Build in this order. Ingestion, standardization, matching, exception resolution, audit trail, continuous reconciliation. Most teams optimize in the wrong order, fix the wrong stage, and wonder why close still takes four days.

Disclosure: I consult closely with Rexi. I've tried to keep this post useful regardless of what tool you use but you should know that going in.

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How to build a reconciliation process that doesn't break when your payment volume scales

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