Anonymising Periodic AML Review Files for Quality Assurance – UK GDPR-compliant anonymisation per Money Laundering Regulations 2017

A periodic AML review file is the customer-risk review record MLR 2017 Regulation 28 requires — documenting risk-rating changes and senior approval decisions. The FCA fined NatWest £264.8 million in 2021 after periodic reviews failed; the NCA received 901,000 SARs in 2022-23, a 15.6 percent increase. anonym.legal pseudonymises customer identifiers so quality-assurance teams can assess review timing and completeness without processing personal data.

When this applies

This task applies when periodic AML review files are assessed by quality assurance, second-line compliance, or external audit to evaluate whether the firm's risk-based review cycles meet the requirements of MLR 2017, and those reviewers need the procedural record rather than individual customer identities.

  1. Upload the periodic AML review file for the relevant customer or customer cohort.
  2. The engine identifies customer names, account references, and any named relationship managers or compliance officers.
  3. Each individual is pseudonymised consistently; risk-rating changes, review triggers, procedural timestamps, and due-diligence uplift requirements are preserved.
  4. Approval authority records and any escalation notes remain in clear text.
  5. A reversible mapping table is produced with UK/EU data residency.
  6. Release the pseudonymised file for quality assurance or audit; restore originals before any regulatory inspection.

What you provide

  • Periodic AML review decision record
  • Updated risk rating and rationale
  • Approval sign-off documentation

Limitations & cautions

  • The tool does not assess whether the review timing and risk-based cycle meet the requirements of MLR 2017 Regulation 28. UK GDPR maximum fines reach up to £17.5 million or 4 percent of annual global turnover under DPA 2018 s.157.
  • Where a periodic review triggers a SAR, the SAR must be processed separately under the SAR workflow and must not be pseudonymised for regulatory submission. POCA 2002 s.330 requires disclosure in the regulated sector when suspicion arises — a 5-year imprisonment risk for failure to disclose.
  • The pseudonymised file is for internal quality assurance; any regulatory production requires the re-identified original. MLR 2017 Regulation 39 requires firms to provide AML training to relevant staff at appropriate intervals.

FAQ

Can I batch-process periodic review files for a full customer cohort?

Yes. Upload multiple review files in a batch. The engine applies consistent pseudonyms to individuals who appear across multiple files in the batch.

Are relationship manager names pseudonymised alongside customer names?

Yes. Named relationship managers and compliance officers referenced in review files are pseudonymised as distinct individuals, separate from the customer pseudonym.

How are risk-rating changes presented in the pseudonymised file?

Risk-rating changes (e.g. 'upgraded from medium to high risk') and the rationale for the change are preserved in clear text. Only the customer's identifying information is pseudonymised.

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.