Credit decision dataset de-identification is the removal of applicant fields from a decision table. Equality Act 2010 s.19 bars indirect discrimination, which is the real risk in a scored dataset where a proxy variable stands in for a protected characteristic, and UK GDPR Art. 22 gives an applicant scored by a solely automated process a right to meaningful review. anonym.plus marks each item on your device, so the analytic signals stay usable while direct identifiers go.
When this applies
A decision table ties applicants to scores, outcomes, and protected-characteristic proxies that an s.19 indirect-discrimination review would test. You strip direct fields before the data trains a model or feeds that review, keeping the score and outcome columns the analysis needs.
How anonym.plus handles it
- Open the decision table in anonym.plus on your device.
- The tool flags names, NI numbers, and contact fields.
- Local OCR reads any scanned supporting pages.
- Keep the score and outcome columns you must analyse.
- Swap or black out the confirmed direct fields.
- Save the clean dataset locally.
What you need to provide
- The decision table (CSV, XLSX, or PDF).
- An operator (Replace keeps the columns readable).
- Optional allow-list for score and outcome columns.
PII & financial identifiers detected
| Category | anonym.plus entity type | Example |
|---|---|---|
| Names | PERSON | applicant id → [APPLICANT] |
| Identifiers | UK_NINO | JK 45 21 77 C → [NINO] |
| Tax reference | NATIONAL_ID | UTR row → [TAXID] |
| Money | MONEY | income field → [AMOUNT] |
| Dates | DATE_TIME | decision date → [DATE] |
| Contact | EMAIL_ADDRESS | contact email → [EMAIL] |
Compliance achieved
- Supports proxy-variable analysis under Equality Act 2010 s.19 (indirect discrimination).
- Keeps the score and outcome signals an Art. 22 UK GDPR review of an automated decision needs.
- Flags special-category proxies UK GDPR Art. 9 treats with extra care.
- Offline work keeps the dataset off any server.
Anonymise credit decision datasets offline — see plans & start free →
Limitations & cautions
Direct identifiers are not the only risk. Score, income, and date combine into quasi-identifiers that can re-link a person under the ICO motivated-intruder test. Generalise those for true de-identification, and keep the alias map turned off; the tool flags fields, it does not itself run the s.19 statistical test for indirect discrimination.
Frequently asked questions
Is removing the applicant id enough for anonymity?
No. Score, income, and date can re-link a person when combined. Generalise or suppress those quasi-identifiers for stronger de-identification.
How does Equality Act 2010 s.19 apply to a scoring model?
Indirect discrimination under s.19 catches a facially neutral rule or score that disadvantages a protected group in practice, even without intent. Keeping the outcome and proxy columns, with direct identifiers removed, is what lets that statistical review happen at all.
Is the dataset uploaded for processing?
No. The app runs locally. For real anonymity, keep the reversible alias map turned off; that has no bearing on whether Art. 22 or s.19 apply to the underlying decision process itself.