Payment reconciliation is a critical financial control process that ensures every transaction—whether incoming or outgoing—matches across your internal records, bank statements and processor files. It’s how you prove that what should have happened with the money actually did.
While the concept is straightforward, real-world payment reconciliation is anything but. Cross-border transactions, fragmented data sources and mounting regulatory pressure have made the process more complex (and business-critical) than ever.
So what does effective reconciliation look like today? In this article, we’ll break down what the process involves, who’s responsible, what tools are used—and crucially, why it’s getting harder by the day.
What is payment reconciliation?
Payment reconciliation is the process of comparing and matching payment data from different systems to ensure accuracy. That typically means aligning internal accounting records with external sources like:
- Bank statements
- Payment processor reports
- Card scheme files
- PSP (Payment Service Provider) exports
- Customer invoicing systems
The goal is to identify and resolve any mismatches, whether due to timing issues, data formatting errors, missing records or fraud. When done correctly, reconciliation enhances financial accuracy, supports audit readiness and upholds regulatory compliance.
What does payment reconciliation involve?
The core steps include:
- Collecting data from all relevant systems (banks, PSPs, processors, internal ledgers)
- Standardising disparate formats
- Matching transactions across sources (e.g. processor vs ledger vs bank)
- Flagging exceptions such as failed payments, reversals or duplicates
- Investigating discrepancies and resolving them
- Generating reports to prove traceability and compliance
It sounds straightforward, but real-world complexity is high—especially when reconciling across multiple payment channels, currencies or regions.
Who is responsible for payment reconciliation?
Responsibility typically sits with the finance or operations team, often with input from compliance, risk and audit stakeholders. But in fintech and high-growth environments, reconciliation can stretch across roles:
- Finance teams ensure accurate records and reporting
- Ops teams chase down anomalies and investigate errors
- Compliance teams validate audit trails and regulatory submissions
- CTOs or product teams may manage the underlying infrastructure
With so many stakeholders involved, visibility and shared standards become critical.
What tools are used in payment reconciliation?
Payment firms use a wide mix of tools and approaches—some more efficient than others. According to Kani’s 2025 Payment Reconciliation Survey:
- 44% of firms rely on a partially automated approach, combining spreadsheets with reconciliation tools
- 28% use dedicated payment reconciliation software, built for scale, automation and auditability
- 12% still use spreadsheets exclusively, exposing themselves to manual errors and reporting delays
- 12% outsource reconciliation entirely to third-party providers
- Only 5% have developed their own in-house solution
The trend is clear: while some firms are still dependent on spreadsheets, the shift toward dedicated, end-to-end automation is well underway.
Why is payment reconciliation so challenging?
Here’s where the real friction lies—and why many firms struggle to scale their reconciliation processes.
1. Data fragmentation
47% of firms in our reconciliation and reporting survey said fragmented data was their top barrier to accurate reporting.
This is unsurprising, given that each bank, PSP, processor or scheme delivers data in a unique format: CSV, ISO20022, PDF, API or even custom structures. Worse still is that field names differ, time zones clash and metadata is often missing.
Without standardisation across diverse sources and formats, even the best automated reconciliation tools will fall short. Fragmented inputs make it harder to create audit trails, investigate anomalies or generate like-for-like reports. Teams waste time mapping fields, translating formats and chasing missing metadata—just to get to a starting point.
2. Partial payments, reversals, chargebacks and FX
Modern payments are rarely a simple one-to-one debit. You’re dealing with a messy mix of partial settlements, failed transactions, refunds, chargebacks, fees, multi-currency FX conversions and time-lagged deposits. Each of these creates variance across transaction records and requires purpose-built logic to reconcile properly.
In our research, respondents named cross-currency matching (23%), chargebacks and refunds (20%) and multi-channel complexity (18%) as their top challenges when matching data. These aren’t niche issues—they’re daily obstacles that manual tools or basic matching engines just can’t keep up with.
That’s why reconciliation today demands more than a one-size-fits-all approach. To stay accurate, firms need systems that can trace the full transaction lifecycle—from original authorisation to final settlement—across multiple sources, currencies and outcomes.
3. Excel isn’t up to the task
More than half (56%) of payments firms still rely on spreadsheets for reconciliation . It’s a revealing (and costly) statistic.
Among spreadsheet users, 94% say they regularly miss reporting deadlines, and 71% admit the process is unnecessarily time-consuming . These manual approaches also introduce operational risk: over half (53%) of respondents said too much of their team’s bandwidth is tied up in creating reports, rather than investigating discrepancies or adding value elsewhere.
Manual reconciliation may once have worked at smaller scales. But today’s high-volume, multi-source payments data requires speed, traceability and repeatability that spreadsheets simply can’t deliver.
4. Regulatory pressure vs operational reality
Payment firms are under growing pressure from regulators to reconcile daily. But our survey found that the average business spends three hours just preparing data before reconciliation can even begin—meaning daily reconciliation would drain 700 hours every year.
This time sink represents a structural bottleneck that impedes growth and stifles innovation. Without clean, standardised data at the outset, even well-resourced teams struggle to meet submission windows, maintain compliance and defend their numbers under scrutiny.
5. Partial automation isn’t enough
Many companies have started automating parts of reconciliation—such as data ingestion or basic matching—but are still stuck in fragmented workflows.
They end up in what we call the “half-automated trap”:
- Automated ingestion
- But manual formatting
- Basic matching
- But manual exception handling
- Automated alerts
- But manual reporting
This patchwork creates more friction than it solves—and breaks the audit trail in the process.
What’s needed? A shift from tools to solutions.
There’s a difference between using a reconciliation tool and adopting a full reconciliation solution: A tool automates part of the process, like matching; a solution transforms your financial operations from end to end.
The most effective payment reconciliation solutions:
- Standardise and validate incoming data
- Connect siloed systems and formats
- Surface errors in real time
- Track every action for audit
- Deliver reporting on-demand
This is especially true for businesses handling multi-channel payments, cross-border transactions and high-volume operations.
Why it all matters
Reconciliation may not grab headlines, but it’s the backbone of financial integrity. When done well, it prevents errors, enables compliance and builds trust with customers, partners and regulators alike.
But in today’s payments environment, traditional methods are no longer enough. As complexity increases, so too does the need for structured data, repeatable processes and tools built to scale.
Whether you’re a fintech startup or a global payments provider, the message is clear: fix the data first, and the rest will follow.
🔍 Looking to streamline your operations?
Learn how Kani’s payment reconciliation software helps automate workflows, consolidate data across sources and eliminate manual data work.
