The case for AI in mortgage broking rests heavily on data. Lenders publish criteria and rates publicly, creating a large and accessible information base. The difficulty lies in cross-referencing multiple criteria points simultaneously – something Phillips believes AI is well suited to handle.
“When you are trying to find a lender that will accept three or more criteria points that can be extremely complex and time consuming and it’s easy to miss opportunities simply because the lenders wording is ambiguous or spread across multiple large documents,” he said. “Humans are simply not great at retaining all that data in their minds at once.”
However, Phillips is candid about where AI falls short. Both brokers and developers spend the majority of their time not solving the core technical problem, but managing relationships – with clients, solicitors, estate agents and accountants, he said. That interpersonal dimension remains beyond AI’s current reach.
The broker also raises a concern about client expectations. Borrowers are increasingly using AI tools to research their options before approaching a broker, which can introduce confusion. “It’s frustrating having to explain why terms the AI spits out are not relevant to UK law or practice or are simply made up,” he said.
The more fundamental obstacle, in Phillips’s view, is cost. He argues that the industry is currently in a “honeymoon period” in which the benefits of frontier AI models are visible to users through products such as Microsoft Copilot and Google Gemini in search results, without the true cost of access being passed on.