Experiment
Prevent, Don’t Reconcile
Spot likely problems while there is still time to prevent them, reserving specialists for genuine ambiguity and risk.
01
Problem to address
Defra often discovers predictable errors after submission, when correction is expensive and specialist time has already been consumed. Regulators become manual validation engines.
02
What the evidence tells us
Current checks can miss serious errors, such as a producer being the wrong size or a 1000× unit mistake, while failing trivial format issues. People need to spot unusual cases before they distort the record, and to flag them for investigation.
03
The proposition to explore
The idea
Move known checks and explainable anomaly signals earlier, so likely problems are surfaced while they can still be prevented rather than reconciled later.
04
Hypothesis
If known errors and anomalies are surfaced at the earliest practical point, fewer bad records will enter the system and regulators can shift effort from reconciliation to material compliance risk.
05
How it could work
Move rules upstream into submission or ingestion. Use thresholds, historical comparison and inspectable anomaly methods first. Route uncertain cases to humans with an explanation of why they were flagged.
This shifts effort:
from correction prevention
and:
from checking everyone investigating what matters.
06
Example: bring it to life
A producer submits packaging data that is inconsistent with their declared organisation size and prior-year tonnage. Instead of accepting it silently, the service asks them to check the values. Only unresolved or suspicious cases reach the regulator.
Today
- Submit
- bad data
- system accepts
- regulator investigates
- spreadsheet
- Companies House
- producer corrects
- resubmit
Tomorrow
SUBMISSION
System flags
We think something may be wrong.
This submission is unusual compared with similar organisations. Here are the three things worth examining.
- Small producer
- Large turnover
- Low packaging tonnage
Check information
07
AI opportunity
Known deterministic rules should stay deterministic. AI is useful only for pattern-based anomaly detection, prioritisation and explaining why a case may deserve review.
08
Potential value
- Less rework and chasing
- Cleaner data entering downstream systems
- More regulatory capacity for genuine non-compliance
- Earlier intervention before errors compound
09
Reduce uncertainty without overcommitting
Smallest meaningful experiment
Select five high-volume deterministic checks from the existing regulator catalogue. Implement them in a prototype submission flow and measure prevented errors, false positives, completion and downstream rework.
10
Open questions and risks
What we still need to learn
- Over-validation can block legitimate edge cases
- AI anomaly flags need explanation and bias review
- Rules must stay aligned with changing regulation