The layers of checks
Most programmes run five layers, in this order. A failure early on stops the later checks running.
Blocking errors vs warnings
Every rule needs a decision: does a failure reject the filing, or flag it for a supervisor to look at? Too many blocking rules and filers spend days in resubmission loops over trivia. Too few and bad data reaches analysts, who then clean it by hand. Plausibility checks should almost always warn rather than block, because they point to something to ask about, not something proven wrong.
Why filers dread validation messages
A typical failure message quotes a rule identifier and a formula expression. Filers then guess at a fix, resubmit and wait. A message that says which value failed, which rule it broke, what was expected and where to look turns a day of back and forth into a ten-minute fix.
Who should own the rules
If only a vendor can change a validation rule, every policy adjustment waits for a software release. Rules belong with the supervisory team, versioned so you can always show which rule a filing passed and when, and tested on real filings before they go live.
Where AI fits, and where people decide
AI is good at the work around validation: explaining a failure in plain language, suggesting the likely fix, grouping similar failures across hundreds of filers so supervisors can see a pattern, and surfacing plausibility questions worth asking.
It should never decide pass or fail. That is the job of a deterministic rule, so the same filing gets the same result every time and the result can be explained to the firm, the board or a court.
- 1Count the rules that block a filing and those that only warn. Is the balance deliberate?
- 2Pull last cycle’s ten most common failures and read the messages as a filer would.
- 3Find out how many filings needed more than one resubmission, and why.
- 4Ask who can change a rule and how long it takes.
- 5Check that every rule is versioned, so you can show which rule a filing passed and when.
Where Line4 fits
Line4 is a SupTech platform for financial regulators. It collects returns in XBRL, Excel and CSV, validates them against your taxonomies and rules at the point of submission, and puts AI to work reading them, with supervisors making every decision. If you work with a standard we have not yet run in production, we will say so plainly and show you how we would approach it.
Questions
What is XBRL validation?
XBRL validation is the set of automatic checks a filing must pass before it is accepted: technical conformance, consistency with the taxonomy, business rules such as totals adding up, regulator-specific filing rules and plausibility checks.
What is XBRL Formula?
XBRL Formula is the XBRL International specification for writing validation rules, called assertions, that test relationships between reported values, for example that a total equals the sum of its parts.
What is the difference between a validation rule and a plausibility check?
A validation rule tests whether data is correct by definition, such as a total adding up. A plausibility check flags values that pass every rule but look unusual, such as a sharp change from last period. Plausibility checks usually warn rather than reject.
Should AI validate regulatory filings?
AI can explain failures, suggest fixes and surface unusual values for review. The pass or fail decision should stay with deterministic rules, so results are repeatable and explainable.