One field changed somewhere.
Vyntra shows you exactly where — and when.
Reconstruct any transaction in minutes, detect out-of-policy field changes, and generate audit-ready evidence for compliance without stitching logs together by hand across a dozen source systems.
DORA
Articles 10–11
Helps evidence payment data lineage
CPS 230
Operational resilience
Audit-ready change logs, on demand
UK CP29
Minutes, not weeks
Reconstructs transaction lineage fast
AMLD6
Field-level change logs
Supports ongoing audit needs
The Payment Integrity Gap
Weeks of stitching logs,
or one lineage view.
When a regulator or auditor asks for proof of what happened to a payment, most banks reconstruct it by hand — pulling logs from a dozen systems, reconciling formats, and chasing down who changed what and when. A Tier-1 bank assembled a single quarter’s evidence across roughly 20 systems with dozens of staff, over weeks. With one connected lineage view, the same evidence pack comes together in hours, with a one-click export — turning an annual fire drill into routine compliance reporting.
Evidence lives across a dozen systems
Reconstructing a single transaction means stitching together logs across every system it touched: different formats, different owners, different retention windows, and disconnected data flows. A Tier-1 bank assembled one quarter’s evidence across roughly 20 systems with dozens of staff, over weeks.
How Vyntra addresses it
- Captures snapshots non-intrusively, as a read-only overlay, at defined interception points
- Correlates events into one end-to-end lineage thread per payment
- Replaces cross-system stitching with a single lineage export — any transaction, reconstructed in minutes
Field changes go unnoticed until it's too late
A beneficiary account, an amount, a routing field — changed somewhere between systems, by format mapping, automated enrichment, or an unauthorized edit. Without field-level comparison, that change surfaces only after settlement, if it surfaces at all.
How Vyntra addresses it
- Compares field values between adjacent steps in the payment flow with bank-defined tolerances to compute differences
- Flags out-of-policy edits for fast investigation, recording the source system and timestamp for every change
- Detects unauthorized or suspicious changes — account-number tampering, unusual insider edits — before settlement, not after losses
Regulators expect answers in hours, not weeks
DORA Articles 10–11, CPS 230, and UK operational resilience expectations all point the same direction: demonstrable, audit-ready control of critical business services — reconstructed on demand, not assembled once a year under deadline pressure.
For banks and other financial services firms, this evidence is increasingly central to regulatory compliance and operational risk management.
How Vyntra addresses it
- Builds a time-ordered audit trail across systems and rails — searchable by ID, counterparty, amount, or system
- Exports regulator-ready packets in clicks, enabling responses to regulators within hours instead of weeks
- Helps evidence payment data lineage relevant to DORA Art. 10–11 and supports CPS 230 and UK CP29 regulatory requirements
Retention and audit needs outlast typical log tools
App logs and process-mining tools are built for conformance, not compliance — they weren’t designed to prove what changed in a payment, when, and why, retrievable years later when a supervisor asks.
How Vyntra addresses it
- Provides cross-system payment data lineage with field-level before/after diffs — not siloed app logs or conformance checks
- Field-level change logs support AMLD6 audit needs, kept with the payment record and retrievable during audits
- Supports the 8–15-year retention most jurisdictions require for transaction data and change logs
How we deliver it
From fragmented logs to
one lineage, end to end.
Vyntra builds a single lineage thread per payment — captured non-intrusively, compared field by field, and ready to export the moment someone asks for it.
Capture
Non-intrusively, as a read-only overlay, taking snapshots at defined interception points; no changes to payment engines or execution systems.
Correlate
Events into one lineage thread per payment: a single connected view in place of a dozen disconnected system logs.
Compare
Field values between adjacent steps, applying bank-defined tolerances to compute differences and flag what falls outside policy.
Evaluate
Differences against change policies, then report: search, export to PDF/CSV, and optional trend views of difference counts by category or severity over time.
Proven at Tier 1 scale
What a Tier 1 global bank achieved with Vyntra.
~20
Systems reconciled by hand, today
Assembling a single quarter’s evidence pack meant pulling logs across roughly 20 systems, with dozens of staff involved.
Hours, not weeks
Time to produce the same evidence pack
A single connected lineage view replaces weeks of manual cross-system stitching with a same-day turnaround.
1-click
Regulator-ready export
Search by ID, counterparty, amount, or system, then export a time-ordered, audit-ready packet in a click.
GET IN TOUCH
Stop stitching logs together.
Get one lineage view, audit ready.
See how Vyntra reconstructs any transaction in minutes, flags out-of-policy field changes as they happen, and turns evidence assembly from a weeks-long fire drill into a one-click export.
FAQs
What is data lineage in banking?
Data lineage in banking shows how transaction data moves between source systems, how it changes at each step, and where it ultimately arrives. It gives financial services teams a connected view of data flows across the payment lifecycle, including the data origin, timestamps, system hand-offs, and data transformations applied along the way.
For payment operations, end-to-end lineage makes it possible to reconstruct a transaction without manually stitching together logs from multiple systems.
How does data lineage support regulatory compliance?
Data lineage supports regulatory compliance by creating a clear audit trail of how transaction data was processed, transformed, and retained. This helps banks respond to regulatory requirements with evidence that is searchable, time-ordered, and linked to the original payment.
The same evidence can support compliance reporting, regulatory reporting, internal audits, and investigations by showing what changed, where it changed, and which systems were involved.
How does data lineage improve data quality and accuracy?
Data lineage helps teams identify where data quality issues enter a payment flow. By comparing field values between adjacent systems, banks can detect missing data, unexpected changes, mapping errors, and other discrepancies that could affect data accuracy.
It also supports root cause analysis by tracing an issue back through the payment flow to the source system or transformation where it first occurred.
What is the relationship between data lineage and data governance?
Data governance defines how data should be owned, controlled, protected, and used. Data lineage provides the evidence needed to apply that data governance framework in practice by showing how data moves across systems throughout its data lifecycle.
Together, data governance and data lineage help financial institutions strengthen controls, improve metadata management, and maintain consistent oversight across complex data architecture and integration environments.
How does data lineage support BCBS 239 and risk management?
BCBS 239, issued by the Basel Committee on Banking Supervision, focuses on effective risk data aggregation and risk reporting. Data lineage can support these objectives by making it easier to trace data back to its source, understand how it was transformed, and assess whether it is complete and reliable.
For risk management teams, this visibility supports impact analysis, risk data aggregation, and the production of more defensible risk reporting. The precise application of BCBS 239 depends on the institution’s regulatory obligations and implementation framework.
Can data lineage work across legacy and modern banking systems?
Yes. Effective data lineage should connect data flows across legacy platforms, modern payment engines, integration layers, and external systems. This includes tracing events through different data models, formats, and processing environments without requiring changes to the underlying execution systems.
A connected lineage view can also support data integration, system migration, digital transformation, and impact analysis by showing how changes to one part of the architecture may affect downstream payment processes.
