The problem
Protection is one of the most important conversations a mortgage broker can have with a client, and also one of the most consistently underserved. The reasons are familiar: protection conversations take time, clients push back, and writing up a proper personalised gap analysis after the fact feels like administrative overhead on top of an already full day. Many brokers either skip the written analysis entirely, or produce a generic document that does not genuinely reflect the client's individual circumstances.
The regulatory expectation is clear. The FCA expects brokers to give suitable advice and to be able to demonstrate the basis for that advice. A well-written protection gap analysis is both good practice and a compliance safeguard. It shows you considered the client's full financial picture, identified the specific risks they face, and made a clear recommendation. If a client later declines cover and something goes wrong, the documented conversation is your protection as much as theirs.
The problem is not knowing what to write. Most experienced brokers know exactly what a good gap analysis should contain. The problem is the time it takes to write it up properly for each individual client, tailored to their specific income, outgoings, dependants, existing cover, and attitude to risk. With AI, that tailoring becomes fast and the compliance-ready output is produced as a side effect of doing the job properly.
The system
Step 1: Gather the client's protection profile (Claude)
Before drafting anything, use Claude to structure the information you need. This is particularly useful for newer brokers or anyone who wants a consistent data-gathering approach.
Prompt example: "I am a mortgage broker preparing a protection gap analysis for a client. To do this properly, what information should I gather about the client? Please give me a structured checklist covering: income, financial commitments, existing protection cover, family situation, health, employment status, and any other relevant factors."
Use this checklist as your client fact-find guide. The more complete the input, the more useful the AI output.
Step 2: Draft the gap analysis letter (Claude)
With the client's details in hand, use Claude to draft the full gap analysis letter. Be specific and detailed in your prompt to get a genuinely personalised output.
Prompt example: "Please draft a personalised protection gap analysis letter for the following client. The letter should identify their financial exposures, explain the risks in plain English, note what existing cover is in place, identify the gaps, and make a clear recommendation for what cover I am recommending and why. Here are the client details: [Name], aged 38. Employed, gross salary £55,000. Partner aged 36, employed part-time, gross salary £18,000. Two children aged 6 and 4. Monthly mortgage payment £1,450 on a £275,000 repayment mortgage. Other monthly commitments: £350 car finance, £120 council tax, £200 utilities. Existing cover: partner has death in service at 2x salary through employer. No life insurance, no critical illness, no income protection in place. Client has expressed concern about the cost of cover but is open to recommendations. Please write in plain UK English, professional but accessible tone, suitable for sending directly to the client."
Review and edit the output carefully. Add any specific recommendations, product details, or compliance language your firm requires. Check that the letter accurately reflects the client's circumstances and your recommendation.
Step 3: Add compliance language and firm branding (Notion AI)
Copy the draft into Notion and use Notion AI to add your standard compliance disclaimers, check for regulatory language requirements, and format the document consistently with your other client communications.
Prompt example: "Please add a compliant disclaimer section to this protection gap analysis letter, noting that this document does not constitute advice but records the broker's assessment of the client's protection needs and the recommendation made. Also add a statement confirming the client's right to seek independent advice. Keep the tone professional and consistent with the rest of the letter."
Create a template version in Notion that pre-populates the standard sections, so each new letter only requires you to fill in the client-specific variables.
Step 4: Store and track (Notion AI)
Build a simple Notion database to track which clients have received a gap analysis, the date it was sent, whether they accepted or declined the recommendation, and any follow-up required. Use Notion AI to help you set up the database structure and automate reminders for annual reviews.
Prompt example: "Please help me design a simple Notion database for tracking protection gap analysis letters. I need to record: client name, date of letter, cover recommended, premium quoted, whether accepted or declined, reason for decline if applicable, and a reminder date for follow-up. Suggest a layout that is quick to update."
The results
Before: A broker writing personalised protection letters from scratch spent 20 to 30 minutes per client on each letter. Across a busy week with ten or more mortgage cases, this added up to two to three hours of writing time, and in practice many letters were either very generic or not written at all.
After: A personalised, compliance-ready gap analysis letter is produced in five to ten minutes per client using this workflow. The letters are notably more thorough and personalised than those previously written manually under time pressure. One broker reported that protection conversion rates improved after implementing this approach, attributing the change to the higher quality and greater personalisation of the written recommendations. The compliance benefit of having a documented gap analysis on file for every client is equally significant.