The new tool uses data from Legal & General (L&G) Surveying Services and other third-party resources.
By aligning this dataset with Bath Building Society’s lending criteria and the expertise of certified surveyors, the engine can quickly determine whether a property qualifies for lending or requires a physical inspection, said Kyle Greck, head of mortgage sales at Bath Building Society.
“We understand how frustrating valuation delays can be for customers and brokers. This new property decision engine speeds up the process and helps us provide answers sooner, which can make a real difference during a property purchase.
“Importantly, it allows us to instruct the valuation earlier in the process and means that most of our mortgage valuations will now be free of charge, offering even better value for our customers and brokers,” he added.
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Fewer follow-ups
Greck said that at present, valuation reports often led to follow-up questions, adding days to the process and creating uncertainty for customers.
The new system removes this step by allowing certified surveyors – who oversee the engine’s outputs – to address queries directly. This results in faster, clearer decisions and fewer delays.
Paula Matthews of L&G Surveying Services said the tool created “a smarter, faster valuation process that will significantly reduce delays and uncertainty for customers and brokers”.
She continued: “This solution allows certified surveyors to focus their time where it adds most value, while delivering quicker, more reliable decisions. We believe it sets a new standard for efficiency in mortgage valuations without compromising on quality or rigour.”
The move follows Bath Building Society’s recent partnership announcement with Sikoia, a fintech also aimed at speeding up transactions, and is part of a general technology transformation programme.
The technology will enable Bath Building Society to automatically validate documents at upload to ensure compliance and completeness, with instant feedback to reduce rework.
It will also extract income and employment data from payslips and supporting documents, then standardise them to support underwriting decisions.
