Pseudonymising Source-of-Funds Files and Bank Statements – UK GDPR-compliant anonymisation per Money Laundering Regulations 2017

A source-of-funds questionnaire is the MLR 2017 Regulation 28 document establishing the origin of transaction funds — required for EUR 15,000 transactions and higher-risk relationships. The FCA fined NatWest £264.8 million in December 2021 and Santander UK £107.7 million in 2022 partly for source-of-funds failures. anonym.legal pseudonymises customer identifiers while preserving the funds-origin narrative so compliance reviewers can assess verification adequacy.

When this applies

This task applies when source-of-funds questionnaires and supporting statements are reviewed by compliance oversight, quality-assurance teams, or external auditors assessing whether the firm has applied adequate verification measures under MLR 2017, and those reviewers have no need to know the identity of the specific customer.

  1. Upload the completed source-of-funds questionnaire and any supporting bank statement extracts.
  2. The engine detects the customer's name, account numbers, sort codes, and any named transaction counterparties in the documents.
  3. Each individual and account identifier is pseudonymised consistently; account type, transaction categories, amounts, and date ranges are preserved.
  4. The funds-origin narrative and the compliance officer's verification notes remain in clear text.
  5. A reversible mapping table is produced with UK/EU data residency.
  6. Release the pseudonymised documents for oversight review; restore originals before any regulatory submission.

What you provide

  • Completed source-of-funds questionnaire
  • Supporting bank statement extracts (PDF or CSV)
  • Compliance officer's verification notes

Limitations & cautions

  • Bank statement images with embedded handwritten annotations may not be fully captured by automated entity detection; a manual review of the pseudonymised output is recommended.
  • Transaction counterparties named in bank statements are pseudonymised; the transaction category and amount are preserved. UK GDPR maximum fines reach up to £17.5 million or 4 percent of annual global turnover (DPA 2018 s.157) — sharing unredacted statements beyond the compliance team creates a material regulatory risk.
  • The tool does not assess whether the source-of-funds evidence provided meets the standard required by MLR 2017 Regulations 27 and 28.

FAQ

Are account sort codes and account numbers pseudonymised?

Yes. Sort codes and account numbers are personal data under UK GDPR when associated with an identifiable individual and are pseudonymised with consistent placeholder references.

Can I use pseudonymised source-of-funds files for AML training scenarios?

Yes. Pseudonymised questionnaires and statement extracts that preserve the funds-origin narrative and transaction patterns are suitable training materials. MLR 2017 Regulation 39 requires regulated-sector firms to provide staff with AML training at appropriate intervals, and realistic pseudonymised case studies support that obligation.

How are joint-account statements handled?

Each named account holder receives a distinct pseudonym; the joint-account structure is preserved in the pseudonymised output.

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 company HQ is in Saarbrücken, Germany. Our servers run in Hetzner's Falkenstein datacenter.

Hetzner holds 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.