Data quality asks
- Is the field populated?
- Is the value in the expected format?
- Does the record meet a threshold or rule?
- How many defects are visible?
Specialist data and control integrity advisory
If the data fails, the decision fails.
DQIntegrity helps regulated organisations prove that decision-critical data is complete, correct, traceable and controlled—from source to outcome.
Conventional data-quality reporting can show whether individual fields meet rules. It does not necessarily prove that the right population arrived, transformations preserved meaning, controls operated, exceptions were resolved or decision outputs can be reconstructed.
6½ yearsRecent sustained focus on financial-crime data strategy, controls and integrity
≈30 jurisdictionsMulti-entity transaction-monitoring data-control environment
End-to-end deliveryPayments, migration, testing, remediation and operational embedding
AI-ready assuranceProvenance, synthetic data, monitoring and reconstructable evidence
Why DQIntegrity exists
Data quality is necessary. End-to-end integrity is the real control question.
Data can pass field-level quality rules and still be incomplete, duplicated, mapped incorrectly, transformed without traceability or omitted from a downstream decision process.
Data integrity asks
- Did the complete expected population arrive?
- Was meaning preserved across every transformation?
- Can decision inputs and outputs be reconstructed?
- Did controls detect, escalate and resolve exceptions?
DQIntegrity treats completeness, correctness, traceability, ownership, control operation and remediation evidence as one connected assurance problem.
Control architecture
Seven linked layers from source to accountable outcome.
The framework can be applied to financial crime, payments, reporting, migration, AI-dependent processes and other decision-critical environments.
Source
Expected population, ownership and permitted use.
Movement
Transfer, ingestion, sequencing and timeliness.
Transformation
Mapping, enrichment, derivation and version control.
Consumption
Which process, rule, model or decision used the data.
Control
Reconciliation, correctness and exception detection.
Evidence
Proof of operation, traceability and accountability.
Remediation
Resolution, root cause and sustainable improvement.
Commercial services
Defined interventions—not generic data-quality programmes.
Data & Control Integrity Diagnostic
Identify structural gaps across data journeys, controls, evidence and ownership.
Explore →Financial Crime Data Integrity Review
Assess whether monitoring, screening and KYC processes receive complete and correct data.
Explore →Reconciliation & Control Design
Design population, value, mapping, correctness and exception controls across the journey.
Explore →AI, Automation & Synthetic Data
Provenance, permitted use, versioning, monitoring and human accountability.
Explore →Priority environments
Where data failure changes a regulated or operational outcome.
Financial crime and transaction monitoring
Completeness and correctness across source systems, data layers, scenarios, alerts and investigation outcomes.
Transaction monitoring →Banking, payments and migration
Transaction populations, value preservation, transformations, testing, cutover and operational reconciliation.
Banking applications →Regulatory and management reporting
Aggregation, lineage, ownership, evidence and decision-usefulness aligned to BCBS 239 and supervisory expectations.
BCBS 239 readiness →ICT and third-party resilience
Data availability, restore integrity, provider dependencies and evidence across critical services.
DORA data resilience →AI and automated decisions
Training, reference and synthetic-data lineage; controlled transformations; drift and exception evidence.
AI assurance →Regional and specialist banks
Proportionate control improvement for CEE, SEE and Western Balkan institutions modernising data and regulation.
Regional banking →Brand architecture
Specialist depth inside a wider senior-advisory model.
Resolvo Advisory
The intended corporate and commercial architecture for specialist propositions and engagements.
NFRisk
Senior advisory across financial crime, data and control integrity, payments and operational resilience.
Explore NFRisk →DQIntegrity
Focused depth in end-to-end data and control integrity, evidence and assurance.
DQIntegrity can support a focused data-integrity mandate directly or operate within a wider NFRisk transformation, assurance or fractional-advisory engagement.
If the data fails, the decision fails.
Start with the point where confidence is assumed but not yet proven.
A confidential first discussion can establish whether the issue needs a focused diagnostic, control design, remediation support or retained specialist advisory.