The business
This is an illustrative example based on common results AI automation produces in this industry — not a specific client engagement.
An independent mortgage brokerage with 3 qualified advisers and one administrator. Handling residential mortgages, remortgages, buy-to-let, and protection products. Leads came from broker referral networks, Google Ads, and a growing stream of direct online enquiries.
Annual completed cases at start of engagement: 184. Revenue approximately $310,000. The principal broker had identified growth as a priority but felt the business was capacity-constrained - more leads were coming in than the team could handle properly.
The real constraint wasn't advisers - it was process
A closer look revealed the bottleneck wasn't adviser time. It was the time spent on pre-advice administration:
- Responding to initial enquiries (often multiple back-and-forth emails before a fact-find was even scheduled)
- Chasing documents from clients who'd been asked for bank statements, payslips, and ID
- Following up with leads who'd enquired but not yet booked a fact-find appointment
- Re-engaging past enquiries who'd gone quiet
The administrator was spending approximately 5 hours per day on these tasks. The advisers were spending another 2-3 hours combined. That's 7-8 hours of professional time daily on work that didn't require human judgement.
What was deployed
A three-component automation system built around the firm's existing CRM:
Component 1: Intelligent enquiry management An AI automation workflow processed all inbound enquiries, categorised them by type (purchase, remortgage, BTL, protection), and sent an immediate personalised response with:
- Acknowledgement of their specific situation
- A link to book a fact-find appointment
- An initial document checklist
Response time dropped from an average of 4 hours to under 3 minutes.
Component 2: Lead nurture sequence Enquiries that didn't book within 48 hours entered a 21-day nurture sequence:
- Day 2: Educational email on the mortgage process and what to expect
- Day 5: Case study relevant to their situation (first-time buyer, remortgager, etc.)
- Day 10: Rate alert email flagging recent market movement
- Day 14: Direct follow-up: "Are you still looking to proceed?"
- Day 21: Final touchpoint with a soft deadline offer
Component 3: Document chasing automation Once a fact-find was booked, the system automatically sent a document request list and chased outstanding items at 3-day intervals - removing the need for advisers or admin to manually chase clients.
Results over 6 months
| Metric | Before | After | Change |
|---|---|---|---|
| Enquiry-to-fact-find conversion | 34% | 58% | +24pts |
| Avg time enquiry to fact-find | 8.4 days | 2.1 days | −75% |
| Completed cases (6 months) | 92 | 140 | +52% |
| Admin time on follow-up | ~5 hrs/day | ~40 min/day | −87% |
| Document turnaround time | 9.2 days avg | 4.7 days avg | −49% |
Revenue increased from approximately $155,000 (H1 prior year) to $236,000 - a 52% uplift on comparable period.
Why this matters for financial services SMBs
Regulated industries often assume automation is incompatible with compliance. In practice, the opposite is true: automation creates an audit trail, ensures every client is communicated with consistently, and removes the human variability that creates compliance risk.
Every message sent by the system was pre-approved and reviewed. The AI handled administration and scheduling - nothing that touches regulated advice.
What clients in this situation typically tell us
Brokers in this position commonly assume they need to hire another adviser. The consistent finding is that the real constraint is administrative repetition, not adviser capacity. Lead nurture sequences tend to recover a significant number of cases that were assumed lost — enquiries that went quiet but never actually lost interest.
Running a financial services business? See how automation works for mortgage brokers and IFAs or get in touch for a free pipeline review.