Anonymize Dodd-Frank §1071 Small Business Lending Data for Review – CCPA/HIPAA-compliant de-identification per 15 USC §1691c-2

Dodd-Frank §1071 (15 USC §1691c-2) requires covered financial institutions to collect and report data on small business credit applications, including applicant demographic information and credit decision details that link directly to individual business owners. anonym.legal pseudonymizes those personal identifiers so compliance teams can audit data collection accuracy and ECOA compliance without processing applicants' personal data.

When this applies

Apply this workflow when §1071 data collection records are reviewed by fair-lending compliance teams, internal auditors, or external advisers assessing data quality, demographic-field completeness, and ECOA compliance posture, and the reviewer does not require the identity of the specific applicant.

  1. Upload the §1071 data collection record or reporting extract — including the application data file and any supplementary demographic collection forms — to anonym.legal.
  2. The engine identifies applicant names, individual business-owner demographic information, Tax Identification Numbers, addresses, and account references.
  3. Each applicant and business owner is pseudonymized with a distinct, consistent placeholder; credit-decision outcome, product type, loan amount requested, business revenue tier, and demographic field completeness indicators are preserved.
  4. NAICS code, census tract, time-to-decision, and pricing information are preserved as non-personally-identifying structural data.
  5. A reversible mapping table is encrypted and stored with US data residency.
  6. Export the pseudonymized data file for compliance review or fair-lending analysis.

What you provide

  • §1071 data collection record or HMDA-adjacent reporting extract
  • Demographic information collection forms completed by applicants
  • Credit-decision documentation with underwriting rationale

Limitations & cautions

  • CFPB submission of §1071 data must include accurate applicant data as required by 15 USC §1691c-2; pseudonymized files are for internal audit only.
  • Fair-lending statistical analysis requires aggregate data, not pseudonymized individual records; this workflow supports review of individual file quality rather than portfolio-level disparity testing.
  • The tool does not assess whether the data collection procedures comply with the CFPB's implementing regulation under §1071.
  • Business entity names associated with the credit application are preserved; only natural-person applicant and owner data is pseudonymized by default.

FAQ

Are demographic self-identification fields pseudonymized or preserved?

Demographic field values — such as sex, race, and ethnicity — are preserved as structural data elements required for §1071 compliance review. Only the applicant's name and direct personal identifiers are pseudonymized.

Can pseudonymized §1071 records be used to train loan officers on data collection procedures?

Yes. Records pseudonymized to remove applicant identities while preserving the data-field structure and demographic collection form are effective training materials for loan officers and compliance staff.

How are principal owner names on the business credit application handled?

Named principal owners are treated as natural persons and pseudonymized with distinct pseudonyms separate from any pseudonym assigned to the business entity applicant.

Financial Services Compliance

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.