Seeing where loans stall before the client does
A pipeline view from enquiry to settlement, with approval times by lender, so brokers know where to chase and what to tell each client.
The problem
A broking team knew its pipeline from the CRM, but not where loans were getting stuck or which lenders were slowing approvals. Clients called to ask for updates the team did not have.
What the numbers say
Slower approvals push settlement dates out, and clients waiting on approval are the most likely to go to another broker.
How FDX uses it
- 01Connect the dataWe connect the CRM and lender status updates into one model, refreshed several times a day.
- 02Draft, then approveAI drafts client updates and document checklists from the file. The broker checks each one before it is sent.
- 03Keep it privatePersonal and financial details are masked before any AI model sees them, with onshore hosting available and no data used for training.
Next steps it points to
- LendersRaise turnaround times with lenders C and E, and offer clients a faster option where it suits them.
- SettlementCheck the 23 approved loans for missing documents. AI drafts each client checklist, and the broker approves it.
- Follow upCall every fact find with no lodgement after 21 days.
ExampleExample case study with illustrative sample data. Not a real client or real results.
Next exampleWealth managementKeeping every client review on time, in a business built on trust
The dashboard is one part of the work.
It gives the team one trusted view of the numbers. The bigger gain is AI working from that same data to draft the follow ups, with a person approving every step.
When AI drafts from your data, personal details are masked before the model sees them. Onshore hosting is available, and your data is not used for training.