The legal and commercial home for independent advisory work.
DQIntegritywithin the NFRisk advisory architecture
Decision-critical data. Complete. Correct. Controlled.
Prove the data journey behind every critical decision.
DQIntegrity helps regulated organisations establish whether the right data reached the right process, completely, correctly and with evidence that can withstand challenge.
The focus is not a single data-quality score. It is the full source-to-decision chain across financial crime, payments, reporting, migration, AI and automation.
Integrated advice across financial crime, data, payments and operational resilience.
Focused data and control integrity expertise within the wider NFRisk architecture.
Why DQIntegrity exists
Strong decisions require proof across the complete data chain.
Regulated organisations often hold extensive data-quality measures yet remain unable to demonstrate that every required record reached the intended control or decision process, that transformations preserved meaning, or that exclusions and exceptions were sustainably governed.
DQIntegrity was developed around this structural problem. It connects business requirements, expected populations, source systems, interfaces, transformations, consuming tools, controls, ownership and evidence—so that data integrity can be demonstrated end to end rather than inferred from isolated checks.
The proposition draws particularly on the principal's most recent six and a half years of focused work in global transaction-monitoring data strategy and control integrity, reinforced by earlier experience across payments, migration, financial crime, governance, testing and operational resilience.
A specialist proposition, not a disconnected business
DQIntegrity is the specialist data and control integrity proposition of Resolvo Advisory, delivered within the wider NFRisk advisory architecture.
Data quality and end-to-end integrity
Data quality is necessary. It is not, by itself, proof of control integrity.
Both disciplines matter. They answer different questions and must be connected where the data supports a regulated, financial or operational decision.
Are individual data elements fit for their defined purpose?
Data-quality activity typically focuses on attributes and rules within a dataset, system or processing stage.
- Completeness of required fields
- Format, validity and consistency
- Nulls, duplicates and uniqueness
- Accuracy against defined references
- Thresholds, scores and issue reporting
Can the complete source-to-decision journey be proven?
Integrity connects local quality to population coverage, controlled movement, transformation, use, ownership and evidence.
- Expected versus received populations
- Count, value and attribute reconciliation
- Mapping and transformation correctness
- Lineage from requirement to decision
- Exception ownership and closure evidence
A dataset may appear locally “green” while records are missing upstream, filtered incorrectly in transit or misunderstood by the consuming process.
The DQIntegrity control architecture
One chain from requirement to decision, control evidence and remediation.
The architecture is adapted to the mandate, but the logic remains consistent: define what should happen, trace what does happen, control the differences and make accountability explicit.
Expected population
Business requirement, scope, critical fields and inclusion logic.
Source and ownership
Authoritative systems, data owners, processors and accountable consumers.
Movement and interfaces
Files, messages, queues, APIs, hand-offs, filters and timing.
Transformation and mapping
Business meaning, field mapping, enrichment, aggregation and exclusions.
Consumption and decision
Monitoring, screening, KYC, payments, reporting, analytics or AI use.
Controls and evidence
Reconciliation, correctness, thresholds, monitoring and retained proof.
Exceptions and remediation
Ownership, triage, impact, correction, root cause and sustainable closure.
Lineage & traceability
Ownership & accountability
Change & operational monitoring
AI & automation assurance
The objective is not more controls everywhere. It is the right controls at the points where data can be lost, altered, misunderstood or used without sufficient evidence.
Diagnose → Design → Mobilise → AssurePriority environments
Where weak data integrity becomes a control, regulatory or decision risk.
Transaction monitoring, screening and KYC
Population coverage, transaction and customer data, reference data, transformations, exclusions, alert inputs, investigation fields and audit evidence.
Payment processing and reconciliation
Message and transaction journeys, count and value reconciliation, scheme and interface controls, rejects, exceptions, settlement and operational readiness.
Regulatory and management information
Lineage from source to report, business definitions, aggregation, completeness, change controls, ownership and evidence supporting executive reliance.
Migration and platform change
Source-to-target mapping, data readiness, conversion controls, reconciliation, testing, cutover, operational acceptance and post-implementation assurance.
AI-enabled and automated decisions
Provenance, representativeness, transformation, feature or rule inputs, synthetic data, versioning, monitoring, human oversight and evidence.
Safe, representative test data
Controlled generation, intended-use boundaries, traceable parameters, scenario coverage, privacy, utility, reproducibility and lifecycle governance.
AI and automation assurance
AI can accelerate the decision—and the consequence of weak data.
An explainable model cannot compensate for inputs whose provenance, completeness, transformation and permitted use are unclear.
DQIntegrity extends the same end-to-end discipline to AI and automated workflows. The focus is not model development. It is the data, control and evidence architecture required to support trustworthy use in a regulated environment.
This includes the relationship between real, anonymised and synthetic data; training, testing and production populations; versioned transformation logic; model or rule inputs; exception handling; monitoring; and accountable human decision points.
Relevant current work
Experience includes synthetic transaction data used to support KYC and monitoring scenarios, data-readiness and assurance considerations for analytics and AI, and current work on provenance, lifecycle evidence and policy controls for synthetic data and AI-enabled processes.
Provenance and permitted use
Where did the data originate, what changed, who approved the use and what restrictions apply?
Population and representativeness
Does the input population reflect the decision context, edge cases and material risk exposure?
Transformation and version control
Can features, rules, mappings, prompts or enrichment be traced to a controlled version?
Synthetic data lifecycle
Are generation method, parameters, privacy, utility, scenarios and limitations documented and reproducible?
Monitoring and human oversight
Are drift, missing populations, exceptions, overrides and accountable review actively controlled?
Audit-ready evidence
Can the organisation reconstruct why the decision was made and which data and control version supported it?
Commercial services
Defined entry points—from focused diagnosis to retained senior assurance.
Each engagement is scoped around the decision, control environment and evidence required. Follow-on work is separately agreed.
Launch serviceSpecialist diagnostic
Control design
Data readiness
Independent assurance
Financial Crime Data and Control Integrity Diagnostic
A focused review of transaction monitoring, screening, KYC or remediation data journeys, controls, ownership and evidence.
Explore this service →End-to-End Data Integrity Review
Establish expected populations, trace source-to-decision flows, test control coverage and identify material integrity gaps.
Discuss scope →Reconciliation and Control Architecture
Define count, value and attribute controls, thresholds, exception governance, evidence and accountable ownership.
Discuss scope →Migration, AI and Automation Readiness
Challenge lineage, mappings, test evidence, provenance, representativeness, synthetic data and operational controls before use or go-live.
Discuss scope →Remediation and Programme Assurance
Provide independent challenge across implementation, evidence, issue closure, testing, readiness and sustainability.
Explore this service → Retained accessFractional Data and Control Integrity Advisory
Ongoing senior support for data-control strategy, regulatory remediation, transformation, governance and executive decisions.
Explore retained advisory →Evidence behind the proposition
Grounded in delivery, not abstract data governance.
Most recent focus
Global transaction-monitoring data strategy and control integrity
Led work connecting expected data populations, source-to-monitoring journeys, completeness, correctness, reconciliation, ownership, exceptions, regulatory evidence and remediation across a complex international banking environment.
6½ yearsFocused most recent experience in transaction-monitoring data strategy and controls.
≈30 jurisdictionsMulti-jurisdiction scope spanning complex retail and wholesale banking environments.
38 entitiesControl and evidence considerations across a large legal-entity footprint.
Source to decisionRequirements, lineage, transformation, controls, exceptions and accountable use considered together.
High-volume payment data and end-to-end readiness
Experience spanning Faster Payments, SEPA, testing, reconciliation, operational acceptance, resilience and controlled introduction into live environments.
Large-scale data migration readiness
Governance, lineage, data quality, testing and readiness challenge for major asset-transfer and platform-change programmes.
Controlled data for advanced use cases
Synthetic transaction data, privacy-aware scenario design, AI data-readiness considerations and emerging provenance and lifecycle controls.
Case evidence is anonymised. Employer and programme references describe prior personal experience and do not imply endorsement of Resolvo Advisory, NFRisk or DQIntegrity.
How the propositions work together
Specialist depth when the data is central. Wider NFRisk challenge when the problem crosses boundaries.
Resolvo Advisory
Provides the commercial and contractual home for independent advisory mandates.
›NFRisk
Connects financial crime, data and control integrity, payments, operational resilience and transformation delivery.
›DQIntegrity
Applies focused end-to-end data integrity, reconciliation, control and evidence expertise.
›How an engagement begins
Start with the decision that must become defensible.
A contained first step establishes what must be proven, what evidence exists and where the material integrity risk may sit.
Define the trigger
Decision, control concern, programme stage, urgency and accountable sponsor.
Identify the chain
Requirements, systems, interfaces, mappings, controls, ownership and current proof.
Test the integrity
Population coverage, correctness, reconciliation, exceptions, governance and impact.
Set the intervention
Priorities, control design, remediation, assurance and separately scoped next steps.
Specialist resources and routes
Explore the proposition in more depth.
Use the specialist brochure for an executive overview, review the defined financial-crime diagnostic, or visit DQIntegrity.com for the dedicated proposition.
Frequently asked
Data integrity and engagement questions
How is DQIntegrity different from a conventional data-quality programme?
DQIntegrity includes data-quality rules but extends beyond them. It tests whether expected populations travel completely and correctly through interfaces and transformations into the consuming process, with reconciliation, lineage, ownership, exceptions and retained evidence.
Is DQIntegrity limited to financial crime?
No. Financial crime is a priority environment because data failure can directly weaken transaction monitoring, screening and KYC controls. The same methodology also applies to payments, reporting, migration, operational processes, AI and automation.
Does DQIntegrity implement data platforms or replace data owners?
No. DQIntegrity provides diagnosis, control architecture, design, challenge and assurance. Engineering, platform implementation and accountable business or data ownership remain with the organisation or its appointed providers.
How does AI fit within the proposition?
DQIntegrity focuses on the data and control conditions that support trustworthy AI and automation: provenance, permitted use, representative populations, transformations, synthetic data, versioning, monitoring, exceptions, human oversight and audit-ready evidence.
Decision-critical data
Make the data journey visible, controlled and defensible.
Discuss the financial-crime control, payment journey, migration, reporting process, AI use case or transformation decision requiring independent senior attention.