Credit Decision Dataset De-Identification with anonym.plus

De-identify a decision dataset for fair-lending analysis while signals stay usable.

Credit decision dataset de-identification is the removal of applicant fields from a decision table. ECOA / Reg B (12 CFR 1002) governs fair treatment in such credit. 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-class proxies. You strip direct fields before the data trains a model or feeds a fair-lending review.

How anonym.plus handles it

  1. Open the decision table in anonym.plus on your device.
  2. The tool flags names, SSNs, and contact fields.
  3. Local OCR reads any scanned supporting pages.
  4. Keep the score and outcome columns you must analyze.
  5. Swap or black out the confirmed direct fields.
  6. Save the clean dataset locally.

What you need to provide

PII & financial identifiers detected

Categoryanonym.plus entity typeExample
NamesPERSONapplicant id → [APPLICANT]
IdentifiersUS_SSN455-21-7763 → [SSN]
National IDNATIONAL_IDITIN row → [TAXID]
MoneyMONEYincome field → [AMOUNT]
DatesDATE_TIMEdecision date → [DATE]
ContactEMAIL_ADDRESScontact email → [EMAIL]

Compliance achieved

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Limitations & cautions

Direct identifiers are not the only risk. Score, income, and date combine into quasi identifiers that can re-link a person. Generalize those for true de-identification, and keep the name map turned off.

Frequently asked questions

Is removing the applicant id enough for anonymity?

No. Score, income, and date can re-link a person when combined. Generalize or suppress those quasi identifiers for stronger de-identification.

Can I keep the outcome columns for analysis?

Yes. Allow-list the score and outcome fields so they stay while direct identifiers are removed.

Is the dataset uploaded for processing?

No. The app runs locally. For real anonymity, keep the reversible name map turned off.