Tools › Entity types

Redact monetary amounts from text, JSON, and CSV

Amounts are not personal data on their own, but a salary next to a name is. Teams use this type when sharing compensation data or customer balances with parties who should see the structure but not the numbers.

How Redactly detects monetary amounts

• Reported when the model flags the value, when an amount follows a cue such as total or salary, or when a currency symbol sits directly against the number.
• Recognises currency symbols, ISO codes, and K/M/B magnitude suffixes.
• Cue-derived amounts have trailing punctuation trimmed, so the span ends at the number rather than the comma after it.

Why redact monetary amounts?

Salary and balance figures are among the most commercially sensitive values in an HR or finance export.

Example request

curl -X POST https://api.bitbanger.in/v1/redact \
  -H "X-API-Key: rdct_your_key_here" \
  -H "Content-Type: application/json" \
  -d '{"text": "Offer letter total compensation: $185,000 base plus $20K bonus.", "style": "replace"}'

Every hit in the response carries entity, text, start, and end. The offsets index into the text you sent, so you can apply the redactions to your own copy of the document. The full response schema is in the API reference.

Questions

Related entity types

• Credit card numbersRedact card numbers from text and JSON with Luhn validation, so only real card numbers are flagged and order IDs are left alone.
• Bank account numbersRedact domestic bank account numbers that carry no checksum and no fixed format.
• Company namesRedact organisation and employer names where the counterparty itself is confidential.