Separating the useful from the promised
AI is being offered to reporting teams as a route to instant insight. In practice its dependable value today is narrower and more operational: summarising, drafting, classifying and accelerating routine preparation.
The figures themselves still need to be derived by defined, auditable logic — not generated.
Where it currently helps
- Drafting first-pass commentary for analyst review
- Summarising long free-text case or complaint records
- Classifying unstructured records into consistent categories
- Suggesting queries and speeding up code preparation
- Highlighting anomalies for a human to investigate
Where it should not be used unsupervised
- Producing a published figure without derivation logic
- Deciding statutory or regulatory classifications
- Interpreting performance for a board without review
- Processing personal data through unapproved services
- Explaining a variance without access to the underlying detail
The assurance principle
Every published figure needs a named human reviewer who can explain how it was derived. AI may assist in producing the narrative around a number, but accountability for the number cannot be delegated to a model.
Controls a board should expect
Named reviewer
Required
Derivation logic
Documented
Personal data
Excluded
unless approved
Use recorded
Auditable
Questions for assurance
- Which reporting tasks currently use AI assistance, and who approved them?
- Is any personal or special category data leaving your environment?
- Can each AI-assisted output be traced to a human reviewer?
- How would you explain an AI-assisted figure to a regulator?
- What happens when the model output and the data disagree?
Any figures shown are illustrative and used to demonstrate an analytical approach. They do not describe a real Coreridge Solutions client.