Regulatory submissions fail for boring reasons. A date entered in the wrong format. A classification code that was retired last financial year. A payment total that does not reconcile because someone adjusted a record after the extract was taken.
None of these represent misconduct or incompetence. They are ordinary data quality failures. But a rejected submission still costs a resubmission cycle, still consumes days of a compliance officer’s time, and — where it happens repeatedly — still shapes how a regulator views the organisation’s control environment.
For self-insured employers, licensees and any organisation lodging periodic data with a regulator, validation is not a technical nicety. It is the difference between a routine obligation and a recurring problem.
Where submission errors come from
Almost all of them originate in one of four places.
Entry. Data captured incorrectly at source — a transposed date, a free-text entry where a code was required, a field left blank because it was not mandatory.
Drift. Data that was correct when entered and is no longer. Classification schemes change. Codes are retired. A category that was valid two reporting periods ago is now rejected.
Extraction. The data is right in the system and wrong in the file. Date formats change on export. Leading zeros disappear. Special characters break the delimiter. Long fields truncate silently.
Timing. The extract was taken Monday, the submission goes Thursday, and three records changed in between. Nothing is wrong with any individual record; the file is simply no longer a true picture.
The fourth is the most insidious because nothing looks broken at any point.
Three layers of validation
Organisations that rarely have submissions rejected almost always validate at three points rather than one. Each layer catches a class of error the others cannot.
Layer one: field validation at entry
Catching errors at the point of capture is dramatically cheaper than catching them at lodgement, because the person entering the data still has the context to correct it. Three weeks later they do not.
Effective entry-level validation means constrained lists rather than free text wherever a coded value is required, date pickers rather than typed dates, mandatory fields that reflect what the regulator actually requires, and range checks on anything numeric. A payment of $150,000 for a single medical expense may be legitimate, but it should require confirmation.
The design tension is real: every constraint adds friction, and too much friction produces workarounds. The resolution is to constrain hard on fields that feed regulatory submissions and stay permissive elsewhere.
Layer two: cross-field logic at record level
Individually valid fields can combine into an impossible record. Layer two tests relationships:
- Date of injury cannot fall after date reported, and neither can fall after date of first payment.
- A closed claim should not have payments dated after closure.
- Injury mechanism and body location should be internally consistent — flag combinations that are physically improbable.
- Where a claim is coded as full incapacity, there should be a current certificate supporting it.
- Payment types should match the claim status they are attached to.
These checks are unglamorous and they find a surprising volume of genuine errors. Run them continuously rather than at submission time, so records are corrected as they arise instead of in a pre-lodgement scramble.
Layer three: submission validation before lodgement
The final layer tests the file against the regulator’s specification before it is sent — structure, mandatory fields, code validity against current schemes, aggregate reconciliation, and completeness against the reporting period.
Self-validating data submission is standard functionality in purpose-built platforms for licensees. Both self-insured claims management systems and Comcare claims management platforms generally build the regulator’s specification into the export itself, so the file is tested against the rules before it leaves the organisation rather than after it arrives.
The distinction matters. A file validated after rejection has already cost you the cycle.
Why “we check it manually” does not hold
Manual review is necessary and it is not sufficient, for a reason that has nothing to do with the competence of the reviewer.
The Office of the Australian Information Commissioner’s breach data makes the general point unusually clearly. In the first half of 2025, human error accounted for 37% of all notifiable data breaches — 193 notifications — with the single largest cause being personal information sent by email to the wrong recipient. The OAIC’s statistics cover the pattern in detail.
These are not careless organisations. They are organisations relying on human attention for a task that human attention performs poorly — repetitive checking at volume, under time pressure, on data that mostly looks fine.
The productive division of labour is straightforward. Automated rules handle volume, consistency and specification compliance. Human review handles judgment: does this pattern look right, does this outlier have an explanation, does anything here contradict what we know about the period?
The audit trail question
Validation controls that cannot be evidenced are worth very little in a review. What a regulator or auditor will typically want to see is which validation rules were applied and when, what exceptions were raised, who cleared them and on what basis, what changed between draft and final, and who authorised the lodgement.
Manual processes struggle to produce this after the fact. Systems that log validation events as they happen produce it automatically — which is the practical reason to run validation inside the system of record rather than in a spreadsheet alongside it.
A pre-lodgement checklist
Before any regulatory submission leaves the organisation:
- Confirm the specification is current. Regulators change formats. Check the version you are validating against rather than assuming.
- Reconcile totals to the source system. Record counts and financial aggregates, both.
- Review the exception report and clear every item. “Known issue” is a decision that should be documented, not an item that stays open.
- Compare against the previous period. Large unexplained movements in claim counts, payment totals or category distribution usually indicate a data problem rather than a real change.
- Confirm the extract timestamp. Know exactly what point in time the file represents, and whether anything material changed after it.
- Record the authorisation. Who approved lodgement, when, and on what basis.
The commercial argument
Validation is easy to under-fund because its output is the absence of a problem. It is worth being concrete about what the absence is worth.
A rejected submission typically consumes several days across compliance, data and management. A pattern of rejections invites closer regulatory scrutiny, and closer scrutiny is expensive in ways that are hard to budget for. For licensees reporting against conditions and performance standards, submission quality is part of how the relationship with the regulator is assessed.
Set against that, the cost of building validation into the system once is modest. It is one of the few compliance investments that reduces ongoing workload rather than adding to it.
Where to start
Pull the last four submissions and list every error, rejection and correction. Sort them by cause using the four categories above — entry, drift, extraction, timing.
The distribution will tell you which layer to build first. Most organisations find their errors cluster in one or two causes rather than spreading evenly, which makes the first improvement both obvious and cheap.
