Anonymize FINRA Rule 3110 Supervisory Review Files for QA – CCPA/HIPAA-compliant de-identification per FINRA Rule 3110

FINRA Rule 3110 requires broker-dealers to establish supervisory systems and conduct reviews of registered representative activity, generating surveillance reports and supervisory exception files that name individual representatives and clients. anonym.legal pseudonymizes those personal identifiers so compliance quality-assurance teams can evaluate supervisory review adequacy without processing employee or client personal data.

When this applies

Apply this workflow when FINRA Rule 3110 supervisory review files — including branch-review reports, correspondence-review logs, and registered representative exception reports — are assessed by CCO-level compliance QA teams, internal audit, or outside counsel evaluating supervisory system design and review coverage.

  1. Upload the supervisory review file or exception report to anonym.legal.
  2. The engine identifies registered representative names, client names, supervisory principal names, and any account numbers or personal identifiers referenced in the review.
  3. Each natural person is pseudonymized with a distinct, consistent placeholder; exception type, review conclusion, escalation outcome, and supervisory principal designation are preserved.
  4. Review dates, branch codes, and product-type classifications remain in plain text.
  5. A reversible mapping table is encrypted and stored with US data residency.
  6. Export the pseudonymized review file for QA or outside counsel assessment.

What you provide

  • Supervisory exception report or branch-review summary
  • Registered representative activity surveillance report
  • Correspondence-review log and escalation records

Limitations & cautions

  • FINRA and SEC examinations require re-identified supervisory review records; pseudonymized files are for internal QA and pre-examination preparation only.
  • The tool does not assess whether the supervisory system design or review frequency meets the requirements of FINRA Rule 3110.
  • Supervisory review files that include client complaints must be cross-referenced with the FINRA Rule 4530 reporting workflow to ensure consistent pseudonymization across both file types.
  • Exception reports produced by third-party surveillance platforms in proprietary formats may require conversion to PDF or CSV before upload.

FAQ

Are both the reviewing principal and the registered representative under review pseudonymized?

Yes. All named natural persons — the registered representative under review, the supervisory principal conducting the review, and any escalation authority — receive distinct pseudonyms with their roles preserved.

Can pseudonymized supervisory review files be used to benchmark branch review quality across offices?

Yes. Files pseudonymized to remove individual names while preserving exception types, review conclusions, and escalation outcomes are suitable for inter-branch quality benchmarking.

How are client names that appear in supervisory exception alerts handled?

Client names in exception alerts are pseudonymized with pseudonyms distinct from the registered representative pseudonyms. The account-type classification and exception-triggering transaction type are preserved.

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.