Anonymising Form D81 Consent Order Statements – UK GDPR-compliant anonymisation per Matrimonial Causes Act 1973

A Form D81 statement of information accompanies a financial consent order under the Matrimonial Causes Act 1973, recording both parties' net income, capital, pension provision, and cohabitation plans alongside personal details. anonym.legal pseudonymises those identifiers so the financial consent structure can be benchmarked or reviewed by a solicitor or costs expert without disclosing the parties' identities.

When this applies

This task applies when a Form D81 and draft consent order are shared with a reviewing solicitor conducting a financial-settlement audit, a costs assessor, or a legal-aid supervisor, and the reviewer requires the financial structure but not the parties' personal details.

  1. Upload the Form D81 and draft consent order to anonym.legal.
  2. The engine identifies both parties' names, addresses, National Insurance numbers, solicitors' details, and any children named in the form.
  3. Each individual receives a consistent pseudonym; net-income figures, capital values, pension provisions, and the financial terms of the proposed order are preserved in clear text.
  4. Lump-sum, periodical-payments, pension-sharing, and clean-break provisions remain intact for the reviewer's analysis.
  5. A reversible mapping table is produced with UK data residency.
  6. Release the pseudonymised documents for review; restore real identities before court submission.

What you provide

  • Form D81 (statement of information for a consent order)
  • Draft consent order in the agreed terms
  • Any pension-sharing annex (Form P)

Limitations & cautions

  • The court-bound Form D81 and consent order must bear the parties' real names — the pseudonymised version is for preliminary review only.
  • Pension-sharing orders reference pension scheme details; scheme names and policy numbers are preserved, but the member's name is pseudonymised.
  • anonym.legal does not assess whether the financial settlement meets the s.25 Matrimonial Causes Act 1973 factors.

FAQ

Yes. This is a primary use case. The pseudonymised D81 can be compared against a library of precedent consent orders without identifying the parties involved.

Yes. Pension-sharing percentage values and the pension scheme's CETV are preserved in clear text. Only the member's name is pseudonymised.

Yes. Mesher and Martin order provisions (including the triggering events and charge percentages) are preserved in clear text; the parties named in those provisions are pseudonymised.

Family Law

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.

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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.
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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.