How should data quality issues be fed back to coverholders?
Send coverholders specific, prioritised data quality findings tied to the affected submission, row or policy reference, expected rule and business impact. Agree who will correct the source, when a response is due and what evidence closes the issue. Use recurring patterns to improve instructions and templates. AI can group and draft findings, while a responsible reviewer confirms their accuracy and tone.
Key takeaways
- An issue is actionable when the submitter can find and understand the affected record.
- Separate source errors from ambiguous rules or mapping mistakes.
- Give a named owner and closure state to each material issue.
- Use repeat trends to improve reporting instructions, not merely to chase submissions.
A validation report can contain hundreds of failed checks while leaving a coverholder unsure what to fix. Poorly framed feedback creates correspondence, late corrected files and repeat errors. The DA team needs a shared way to turn an exception into a clear request that reaches the party able to correct it.
Classify the issue before contacting the submitter
Identify the submission, reporting period, contract reference, record identifier, field and rule. Distinguish missing source data from a mapping error, an unclear reporting requirement and a legitimate business exception. Check whether the value is truly wrong before asking the coverholder to change it. Prioritise issues that block processing or materially affect oversight, accounting or customer outcomes.
Make feedback usable
Traditional control teams use exception reports, shared logs and regular calls. These can work well when they include a precise example, the expected interpretation, the reason it matters and a secure route for questions. Avoid sending sensitive policy or claims details through an unsuitable channel. Group repeated instances of the same root cause, while retaining record-level references so the submitter can act.
Use AI to organise patterns carefully
AI can cluster similar exceptions, translate varied field labels into plain-language issue descriptions and suggest a draft note. It can also compare new issues with previously resolved examples. The DA reviewer should verify the cited records and rule, because a fluent explanation can still misstate the requirement. Do not use AI to attribute blame or silently alter the source file.
Close the loop and prevent recurrence
Assign an owner on both sides, a due date appropriate to the reporting cycle and a state such as queried, corrected, accepted or waived. Record the accepted resolution and whether downstream data must be replaced. Trend repeat issues by rule, template and coverholder. If the same ambiguity recurs across several submitters, revise the reporting instruction or validation rule rather than issuing the same request indefinitely.
Example
In a hypothetical UK insurer’s monthly premium reporting cycle, several coverholders submit blank transaction types for return premiums. The data team first checks the agreed specification and confirms that the field is required for these rows. It sends each reporting contact the affected references, rule and secure correction route. After correction, the team updates its examples in the reporting guide and monitors whether the issue recurs.
FAQs
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Should every failed rule be sent to a coverholder?
No. First confirm whether the cause sits in source data, an internal mapping or the rule itself; prioritise meaningful issues.
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What if the coverholder disputes the rule?
Keep the issue open for joint interpretation by the contract and data owners. Record the decision and update the instruction if necessary.
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Can AI send feedback automatically?
Drafting and grouping can help, but a reviewer should confirm factual accuracy and the correct recipient before sending.
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