Payment reconciliation automation for fintech and retail

Fintech and retail companies reconcile across more payment service providers than any other sector, including Stripe, PayPal, Adyen, and dozens more simultaneously. Manual reconciliation breaks at this volume and complexity. Automated payment reconciliation ingests all PSP feeds simultaneously, matches transactions at scale, and surfaces only genuine exceptions. 

Solvexia is the proven platform for this, reconciling millions of transactions in just minutes. 7-Eleven used Solvexia to reconcile 500,000 transactions per day while Emma Sleep used it for 100,000 transactions per day across 30+ payment service providers, with 500% faster processing than their previous manual process.

This article covers the specific challenges of multi-PSP environments, what payment reconciliation automation looks like in practice, and which tools handle this complexity at scale.

How do fintech and retail companies reconcile high volumes of payment transactions?

Payment reconciliation automation for fintech and retail companies is structurally different from general finance reconciliation across three dimensions:

1. Volume

Retail and fintech companies may need to reconcile thousands or hundreds of thousands of transactions each day. As businesses grow, transaction volumes can quickly exceed what manual processes can handle. 

Gymshark, for example, grew from around 500 orders per day to 4,000–5,000, with Black Friday volumes reaching 30,000 orders in a single day, according to Peoplevox.

2. PSP fragmentation

Enterprise merchants and ecommerce companies use four or more payment processors on average, according to Statista. 

Banking Circle found that 62% of surveyed merchants in the UK, Germany and the Netherlands work with two or three PSPs, often to support different geographies, currencies and cross-border payment requirements.

In practice, a merchant might use different providers for online card payments, digital wallets, international transactions, BNPL and local payment methods.

Each provider may use different data formats, settlement schedules, fee structures and chargeback processes, increasing reconciliation complexity.

3. Settlement timing complexity

A payment processed on Monday may settle on Wednesday as part of a net amount that includes fees, chargebacks and refunds. Matching individual gross transactions to settlement batches and then matching those net settlements to bank entries requires multiple levels of reconciliation.

At high volumes, this becomes difficult to manage manually. Finance teams may need to download reports from multiple PSPs, standardize different data formats, match transactions to settlement batches and reconcile net settlements against bank deposits. Automation platforms address this by consolidating payment data, applying matching rules and routing exceptions for review.

What is the best payment reconciliation software for companies with multiple PSPs?

The right tool depends on your reconciliation complexity and transaction volume. Here is an honest breakdown by use case:

Best for High-Volume Multi-PSP Environments (Fintech, Retail, Travel): Solvexia

Gartner Peer Insights lists Solvexia in its Financial Reconciliation Solutions market, with reviewers highlighting its ability to match large volumes of data and automate repetitive accounting work. 

The no-code platform ingests and standardizes data from payment gateways, banks, ERPs, and internal records simultaneously, applies configurable matching rules, and routes only genuine exceptions for review. 

Emma Sleep uses it to reconcile 100,000 daily transactions across 30+ PSPs. Solvexia states finance teams can typically configure their first workflow within weeks, without IT.

Best for Enterprise AR Reconciliation with Payment Matching: HighRadius

HighRadius is strong for enterprise accounts receivable automation with payment matching. Implementation typically takes 3 to 6 months. It’s typically the best fit for large enterprise teams with dedicated AR resources.

Best for Bank-Heavy Reconciliation with Lower PSP Complexity: ReconArt

ReconArt is strong for bank statement matching and general ledger reconciliation. It’s less suited to high-volume multi-PSP environments.

Best for Enterprise Close Management (not payment matching): BlackLine

BlackLine is built for compliance-heavy close management, not multi-PSP payment volume. Implementation takes 3 to 6 months. It’s a better fit for audit and sign-off workflows than payment reconciliation at scale.

Overall, for fintech and retail where the pain is reconciling across multiple PSPs at daily transaction volume, Solvexia is the direct answer. For enterprise AR automation, HighRadius is the stronger fit.

How do you reconcile Stripe, PayPal, and 30 other payment gateways simultaneously?

Every PSP exports differently. Stripe delivers transaction-level CSVs, PayPal sends net settlement files, Adyen uses a proprietary format with chargeback records embedded, and Klarna settles on a 14-day rolling cycle. 

No two sources share a common reference format, which is what makes manual multi-PSP reconciliation unworkable at scale. Here is how Solvexia solves it:

Step 1: Connect all PSPs once

Solvexia connects each payment provider via API or file-based integration. No manual downloads required after initial setup.

Step 2: Simultaneous ingestion

Solvexia pulls all PSP feeds on a scheduled run simultaneously, not sequentially. Format normalization is applied at ingestion, before matching begins.

Step 3: Multi-level matching

Solvexia matches gross transactions to PSP settlement batches to net bank deposits. Fees, chargebacks, and refunds are reconciled as part of the matching logic, not as separate manual steps.

Step 4: Exception surfacing

Solvexia surfaces only genuine mismatches to the reviewer. Customers report 98% fewer reconciliation errors than with manual processes, allowing reviewers to focus on a small set of genuine exceptions.

Step 5: Daily close

Reconciliation can be completed daily, not monthly. Discrepancies are caught within 24 hours rather than discovered at month-end.

Solvexia implements this process natively, connecting to all PSPs simultaneously, handling multi-level settlement matching from gross transaction to batch to net bank, and surfacing only exceptions for daily review. 

Emma Sleep uses exactly this workflow: 100,000 transactions per day across 30+ PSPs and 80 different file types, all matched automatically. According to Nilus, finance teams automating this process reduce manual reconciliation effort by 40+ hours per month.

What does payment reconciliation automation look like for a retail finance team?

Three Solvexia customers illustrate what this looks like in practice across ecommerce, retail, and fintech:

1. Emma Sleep (global ecommerce, direct-to-consumer)

In this case study, Emma Sleep needed to process 100,000 transactions per day across more than 30 PSPs and approximately 80 payment-provider file types, each requiring standardization before matching against ERP sales data.

Before Solvexia, reconciliation could only be completed weekly, the existing matching tool could not accommodate Emma Sleep’s increasingly complex requirements, and manual transaction matching was outsourced to a BPO provider.

With Solvexia, Emma Sleep automated data standardization and applied bespoke rules to match PSP payments against ERP records. Reconciliation is now completed daily, the matching ratio has increased, and the process runs 500% faster. The automation also eliminated the need to outsource manual transaction matching.

2. 7-Eleven (retail, high-volume POS and payment gateway)

In this case study, 7-Eleven reconciled 500,000 transactions across two payment gateways, its point-of-sale system, bank records and more than 3,400 stores. After implementing Solvexia, reconciliation reports that previously took up to two days to complete could be produced in minutes, making the process up to 100 times faster.

3. Tala (fintech, global payments)

In this case study, Tala operated across several international markets, but its reconciliation methods varied by market. Some transactions were manually matched in Excel, while other markets relied on month-end balance comparisons. Reconciling an individual payment rail could take two hours or longer.

With Solvexia, Tala automated daily reconciliation across nine cash-in and cash-out payment rails. The platform validates, prepares and matches data from different rail formats using one-to-one and one-to-many rules. Each reconciliation now runs in 10–15 minutes—up to 12 times faster—and discrepancies can be investigated promptly.

Across these case studies, much of the reconciliation burden occurs before matching begins: collecting, standardizing and preparing data from different payment sources. Solvexia automates these steps alongside transaction matching, reducing the manual work required throughout the process.

How do you handle many-to-many payment matching at scale?

Many-to-many matching is where most fintech payment reconciliation tools fail in high-volume payment environments.

What many-to-many matching means in practice

In retail payment reconciliation and fintech, a single bank deposit rarely corresponds to a single transaction. A PSP settles multiple individual transactions into one net batch payment, which reaches the bank as a single line after fees and chargebacks are deducted. Matching requires:

• One bank entry to one PSP settlement batch
• One PSP settlement batch to multiple individual transactions
• Multiple chargebacks and refunds to multiple original transactions
• Fee reconciliation across the batch

Why most tools fail here

Basic reconciliation tools handle one-to-one matching only. Multi-PSP environments generate many-to-many scenarios at scale. Tools that cannot handle this leave complex matches as manual exceptions, which in a 100,000-transaction-per-day environment means thousands of daily manual items.

What good many-to-many matching looks like

With good many-to-many matching, you see: 

• Configurable grouping rules: Batch payments grouped by PSP, date, and currency before matching
• Fee and chargeback netting: Handled as part of the matching logic, not separate processes
• Tolerance rules: Small rounding differences auto-cleared rather than flagged as exceptions
• Exception rate target: Under 2 to 5% of transactions even in many-to-many environments

Solvexia handles all four natively: configurable grouping rules, fee and chargeback netting within the matching logic, tolerance rules for rounding differences, and a target exception rate under 5% even in 30+ PSP environments. The Emma Sleep workflow handles this complexity daily at 100,000 transactions.

How does Solvexia handle payment reconciliation across multiple PSPs?

Solvexia is a purpose-built reconciliation platform built for complex multi-PSP, high-volume reconciliation challenges that fintech and retail finance teams face. 

Here is how each layer works:

1. Data ingestion

Solvexia’s payment gateway reconciliation software connects to PSPs, bank feeds, and accounting systems simultaneously via API, SFTP, or file-based connectors. Supported PSPs include Stripe, PayPal, Adyen, Klarna, and Square, with custom connectors for less common providers. All 80+ file types ingested by Emma Sleep are handled natively, with format normalization applied at source.

2. Matching engine

Solvexia enables rule-based matching configured by finance without IT involvement. It supports one-to-one, one-to-many, and many-to-many matching, with multi-level matching from gross transaction to PSP batch to net bank settlement. Fee, chargeback, and refund netting are handled within the matching workflow.

3. Exception handling

Genuine exceptions are routed automatically to the right reviewer with full context: transaction detail, PSP record, bank entry, and reason for mismatch. Resolution is logged automatically.

4. Audit trail

Every run is logged automatically, with sources ingested, transactions matched, exceptions flagged, and resolutions recorded. Solvexia is compliant with APRA and internal audit requirements.

Scale proof points

• Emma Sleep: 100,000 transactions matched daily across 30+ PSPs and approximately 80 file types; reconciliation completed 500% faster.
• 7-Eleven: Reconciliation across two payment gateways, its POS system and bank records for 3,400+ stores; completed up to 100 times faster.
• Tala: Daily reconciliation automated across nine cash-in and cash-out payment rails; processing reduced from up to two hours per rail to 10–15 minutes, making it up to 12 times faster.

Solvexia is a no-code finance automation platform that helps mid-market and enterprise teams automate complex reconciliations, regulatory reporting, and financial close processes without IT involvement. Acquired by Ripple Treasury (formerly GTreasury) in January 2026, Solvexia is now part of a platform trusted by 1,000+ customers in 160 countries.

What are the most common payment reconciliation problems at scale?

Most fintech and retail finance teams encounter the same failure modes as transaction volume grows:

1. Settlement timing mismatches: Payment processed Tuesday, settled Thursday, bank recorded Friday. Flagged as unmatched for two days and manually cleared every cycle.
2. PSP fee and chargeback netting: The bank receives a net deposit; transaction records are gross. Reconciling net to gross requires downloading fee schedules, chargeback logs, and refund records separately for every PSP every day.
3. Reference number fragmentation: The order ID in the ecommerce platform, the transaction ID in Stripe, and the reference in the bank feed are all different. No common key to match on.
4. Volume spikes break manual processes: Black Friday, Cyber Monday, and promotional events generate 5 to 10x normal daily volume. Manual teams cannot scale to meet the spike and backlogs accumulate.
5. Multi-currency complexity: International retail processing GBP, EUR, USD, and AUD across different PSPs with different exchange rates applied at different times. Each currency adds a reconciliation layer.
6. Chargeback and dispute matching: Disputed transactions create many-to-many scenarios across the original transaction, chargeback, response, and resolution, each in a different system.
7. Daily vs monthly cadence mismatch: PSPs settle daily; most finance teams reconcile monthly. A 30-day backlog of unmatched items at month-end is structurally guaranteed by this mismatch.

How do you maintain accuracy when payment volumes spike?

Volume spikes expose the fundamental weakness of manual reconciliation: it scales linearly. Double the volume means double the team or double the time.

Why manual breaks at scale?

A team reconciling 1,000 transactions per day can absorb a 20% spike. A Black Friday 5x spike on 10,000 daily transactions produces 50,000 items that cannot be manually reconciled without errors or delays.

How automation handles spikes?

Automated reconciliation has a flat cost curve. The same workflow that runs on a normal day runs on Black Friday. 

Solvexia's workflow architecture is volume-agnostic in that the same configuration that reconciles Emma Sleep's daily 100,000 transactions handles spike days without rule changes or manual intervention. The matching rules process whatever volume is presented on the scheduled run.

What to configure before a spike?

• Tolerance rules adjusted for promotional pricing rounding differences
• Extended settlement timing windows for PSP lag during promotions
• Exception routing confirmed so spike volume reaches reviewers with capacity
• Daily exception rate dashboard monitored: a spike in exception rate signals a rule that needs updating, not a reason to revert to manual

Realistic accuracy target during spikes

During high-volume events, such as Black Friday or major sales campaigns, automated reconciliation platforms are designed to scale with transaction volume. 

Well-configured reconciliation processes typically achieve 90 to 95% auto-match rates, with performance depending primarily on the quality of matching rules and source data rather than transaction volume itself, according to Nilus and Transformance.

Conclusion

Payment reconciliation across 30+ PSPs, daily transaction volumes, and multi-currency settlement is not solvable with manual processes or general-purpose reconciliation tools. The companies doing this well, Emma Sleep, 7-Eleven, and Tala, use payment reconciliation automation that ingests all sources simultaneously, handles many-to-many matching natively, and surfaces only genuine exceptions for review. 

Solvexia is a no-code reconciliation and financial automation platform built for multi-source, high-volume reconciliation without IT involvement, serving fintech, retail, financial services, and healthcare as part of GTreasury. To go deeper, see how reconciliation software compares across the market or what account reconciliation software actually is.