Since 2023, Kani Payments has been quietly running its Data Scorecard: a first-of-its-kind benchmarking tool designed to assess the operational readiness of payments data.
After reviewing data from 31 leading processors over the past 24 months, the results show the majority of files analysed failed to meet key standards across reconciliation, reporting and fraud oversight. The findings reveal deep-rooted challenges facing the payments ecosystem—challenges that many providers may not even know exist.
What is the Kani Data Scorecard?
The Kani Data Scorecard is a structured, standards-driven framework that evaluates whether payments data is fit for purpose. It tests data against card scheme requirements (e.g., Mastercard/Visa), ISO standards and real-world operational use cases, translating technical gaps into clear business risk.
Each dataset is assessed across categories including:
- General structure – e.g., machine readability, identifiers, format consistency
- Information security – e.g., tokenisation, masking, PII handling
- Data attribution – e.g., ability to link transactions to the correct customer or instrument
- Transaction-specific metadata – e.g., interchange fees, lifecycle tracking, currency fields, BINs/ICAs
Every flagged issue is linked to a downstream impact: whether it blocks reconciliation logic, introduces risk in fraud detection or prevents accurate regulatory submissions.
Scoring Methodology & Results
To make results easily interpretable, the Scorecard uses a colour-coded model—Green (meets standard), Yellow (minor gaps), Orange (major deficiencies) and Red (critical failures). Of the 31 processors reviewed, over 70% fell short of a Green rating, indicating that widespread deficiencies remain even among established providers.
Key Findings
Across the 31 processors tested, the Scorecard surfaced three common patterns of failure:
1) Misunderstood standards
Many files confuse critical terms—such as settlement vs reconciliation dates, or billing vs settlement currencies—leading to inconsistent logic and downstream mismatches.
2) Missing metadata
Fields required for compliance or fraud monitoring (e.g., 3DS authentication flags, BINs, interchange fees) are often omitted or mislabelled, exposing firms to avoidable regulatory or financial risk.
3) Structural fragility
Some formats are not machine-readable, rely on free-text fields or lack transaction lifecycle markers, which creates barriers to automation and requires costly manual workarounds.
Real-world impact
Firms operating with deficient processor data are at increased risk of:
- Failed or delayed FCA REP-017, AOEM, Mastercard QMR and Visa GOC filings
- Inability to trace funds accurately for safeguarding
- Missed fraud patterns due to incomplete transaction lineage
- Time-consuming reconciliation backlogs that delay month-end close
As Aaron Holmes, CEO of Kani Payments, puts it:
“We’ve seen companies struggle for years with data they assumed was ‘good enough’. But our Scorecard shows that many failures are baked in before reconciliation even begins. The cost isn’t just inefficiency—it’s invisible risk.”
High-performing processors
The Scorecard also surfaced a group of processors that consistently demonstrated strong data hygiene and completeness across all categories. Firms like Thredd, Enfuce, DPG, Paymentology and Clowd9 stood out for their structured formats, rich metadata and alignment to scheme and regulatory standards.
These providers are already realising the benefits of getting data right at the source:
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Faster, cleaner reconciliation with minimal manual intervention
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Reliable population of regulatory reports like FCA REP-017, QMR and GOC
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Stronger fraud detection through complete transaction lineage and 3DS metadata
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Better transparency into scheme fees, settlement flows and safeguarding positions
In short, high-quality payments data sits at the heart of reconciliation, fraud prevention and regulatory compliance. When it’s accurate, complete and well-structured, it drives faster operations, reduces risk exposure and builds trust across every part of the value chain.
What this means for the industry
The results are not about naming and shaming—they’re a wake-up call. In an industry built on trust, data quality must be as much a priority as security or authorisation performance. And yet, standards remain fragmented, inconsistently applied and often undocumented.
Kani believes that data quality assurance should be a procurement requirement for any regulated fintech. The Scorecard offers a way forward—a common framework to identify gaps, drive improvements and raise the bar for everyone in the ecosystem.
Poor-quality data leaves institutions exposed. It can result in inaccurate FCA REP-017 submissions, incorrect safeguarding allocations and missed indicators of suspicious activity. These gaps not only compromise regulatory compliance but also trigger audit failures, reporting discrepancies and an overreliance on manual fixes.
The way forward
Some processors already demonstrate that high-quality, compliant data is both achievable and sustainable. But across the industry, a clear divide is emerging between those building resilient data infrastructure and those relying on outdated workarounds that won’t withstand rising regulatory scrutiny.
Kani’s Data Scorecard sets a new benchmark, linking technical precision to operational trust. In a landscape where scale, compliance and speed are non-negotiable, the goal is simple: ensure every institution can trust the data they rely on.
