Pseudonymising Source-of-Wealth Files for AML QA – UK GDPR-compliant anonymisation per Money Laundering Regulations 2017

Source-of-wealth questionnaires gather biographical and financial history from high-risk or PEP customers to explain the origin of their overall wealth. These documents contain highly sensitive personal data including employment history, inheritance details, and business ownership records. anonym.legal pseudonymises those personal identifiers — preserving the wealth-origin narrative and verification-adequacy context — for compliance quality review.

When this applies

This task applies when source-of-wealth questionnaires and supporting evidence are reviewed by compliance oversight, internal audit, or training facilitators who need to assess procedural adequacy without accessing the specific customer's wealth history and personal biographical data.

  1. Upload the completed source-of-wealth questionnaire and any supporting evidence summaries.
  2. The engine identifies the customer's name, biographical details, employment history references, named business interests, and any named family members referenced in inheritance or gift explanations.
  3. Each individual is pseudonymised consistently; wealth-origin category, evidence type, and compliance-officer verification notes are preserved.
  4. Business entity names referenced as wealth sources are preserved unless you flag them for pseudonymisation.
  5. A reversible mapping table is produced with UK/EU data residency.
  6. Release the pseudonymised questionnaire for quality review; restore originals before any regulatory submission.

What you provide

  • Completed source-of-wealth questionnaire
  • Supporting evidence summary or inventory
  • Compliance officer's adequacy assessment notes

Limitations & cautions

  • The tool does not assess whether the source-of-wealth evidence provided is sufficient for the risk level of the customer.
  • Employment history references that contain employer names are pseudonymised at the individual identifier level; the employer name may be preserved unless flagged.
  • The pseudonymised questionnaire is for internal review only; regulatory submissions require the re-identified original.

FAQ

Should I pseudonymise employer names as well as the customer's name?

Employer names are typically corporate identifiers rather than personal data. However, if the employer name would readily identify the individual in context, you can flag it for pseudonymisation before processing.

How are inheritance or gift explanations handled when they name the donor?

Named donors or family members referenced in inheritance or gift explanations are pseudonymised as distinct individuals with consistent pseudonyms throughout the document.

Can a pseudonymised source-of-wealth questionnaire be used in external AML training?

Yes, provided the pseudonymisation is robust enough to prevent re-identification in the training context. For external training, obtain legal advice on the adequacy of pseudonymisation for the specific disclosure context.

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.