Quality control technology
Quality control technology, not quality control people. The platform provides the sampling, checklists, defect capture, rework loops and management information your first and second line use to evidence that CDD work meets the standard. The reviewers and assurance staff are your firm's own.
- Configurable sampling by reviewer, case type, risk band or defect history.
- A structured defect taxonomy instead of free-text review notes.
- Rework and recheck tracked against the original case.
- Quality management information drawn from the live record, not a monthly return.
Why quality assurance data is usually unusable
Most firms run quality checks. Rather fewer can use the results. Where findings are recorded as free text, there is no way to tell whether the same defect is recurring across teams, whether a policy change fixed it, or which part of the process is actually generating failures.
Sampling is the second problem. A flat sample rate applied to everyone spends the same effort on well-evidenced routine files as on the complex ones, and does not adjust when a reviewer's quality improves or deteriorates. The sample ends up defensible on paper but poorly targeted in practice.
The third is separation. Where quality control sits in a different system from the work, the link between a failed check and the corrected file is manual. The firm can show that checks happened and that files were completed, but not that this file failed, was corrected in this way and was rechecked by this person.
Note the distinction this page draws throughout: Agora supplies quality control technology. Independent assurance and the judgement about whether work is good enough remain functions of your firm.
What the quality control layer does
The framework is yours. The platform makes it operable and measurable inside the same workflow as the work itself.
Configurable sampling
Sample rates set by reviewer, case type, risk band, product or recent defect history, so checking effort follows risk and observed quality rather than a flat percentage.
Structured defect taxonomy
Findings recorded against defined defect categories and severity levels, making failures countable, comparable and trendable across teams and periods.
Check templates aligned to the standard
Quality checklists reflect the review standard in force, and the platform records which version of the checklist was applied.
Rework and recheck loops
A failed case returns to the reviewer with the defect attached, and the correction is rechecked, so failure, remedy and confirmation sit in one record.
Consistency measurement
Because checks are structured, outcomes can be compared across reviewers, teams and case types to show where the standard is being applied differently.
Independent assurance support
Second line and internal audit can run their own sampling and checks over the same population with their findings held separately from first line quality control.
Quality management information
Pass rates, defect distribution, severity mix, repeat defects, rework ageing and reviewer trends reported from live data and drillable to the individual case.
Evidence of the quality process itself
The quality record exports with the case, so a supervisor can see not only the due diligence performed but the checking that was applied to it.
Controls and evidence
Quality assurance is itself a control that gets tested. These properties are what make it stand up.
Separation of doer and checker
Workflow roles prevent a reviewer from quality-checking their own case where your configuration requires it, and record which role performed each action.
Attributable findings
Every defect records who raised it, against which checklist version, with what severity and what supporting rationale.
Closed-loop remediation
A defect cannot simply disappear; the record carries the correction and the recheck outcome.
Sampling that can be evidenced
The sampling basis is configured, versioned and reportable, so the firm can show why a given population was sampled the way it was.
Trend evidence for governance
Defect trends support a documented case for policy, training or configuration change, and for showing a supervisor that findings led somewhere.
What firms use it to achieve
Firms use the quality layer to turn checking into evidence of control effectiveness.
- Defect data that supports a root-cause conversation rather than a count of errors.
- Sampling effort targeted where quality risk actually sits.
- A demonstrable link between a failed check, the correction and the confirmation.
- Visible consistency, or visible inconsistency, across reviewers and teams.
- Quality reporting available continuously rather than assembled after period end.
Implementation considerations
- Agree the defect taxonomy before go-live; retrofitting categories destroys the comparability of earlier data.
- Decide severity definitions and what severity triggers escalation or a policy review.
- Set the sampling model deliberately, including whether accreditation reduces sampling and what reverses it.
- Keep first line quality control and independent assurance separated in configuration as well as in the target operating model.
- Agree who owns quality management information and what action thresholds attach to it.
Related practitioner guidance
The assurance framework behind this technology is set out in our practitioner guidance.
KYC quality assurance
Sampling, defect taxonomies, thresholds, remediation loops and reporting.
Regulator-defensible CDD audit trail
What a reviewer needs to reconstruct the decision and the checking around it.
Reducing screening false positives
Where quality data and screening calibration meet.
Customer due diligence automation
The governance that keeps an automated process defensible.
Frequently asked questions
Does Agora provide QA staff or an outsourced checking team?
No. Agora provides quality control and assurance technology. The people who perform checks, reach conclusions and hold the accountability are your firm's own first and second line.
Can second line use the same tooling without compromising independence?
Yes. Independent assurance can sample and test over the same population with findings recorded separately from first line quality control.
Can sampling rates vary by reviewer?
Yes, where your framework uses an accreditation model. Sampling can be configured by reviewer, case type, risk band or recent defect history.
Next step
See the quality layer against your own framework
Bring your defect taxonomy, sampling model and a few recent findings, and we will show how the checking, rework and reporting would be configured on the platform.