But with an applicant ’ s patience understandably limited , insurers are increasingly caught between consumer expectations and the realities of the system they are working with . With the data submitted often incomplete , and a human link in the chain at almost every significant step , the journey from application to resolution is dependent on manual intervention – and inevitably slow .

As a result , consumer satisfaction is falling . US analytics company , JD Power , suggests that insurers are only meeting customer expectations 34 % of the time . The average consumer has learned that other industries can automate and accelerate processes using the right technologies – even if they don ’ t necessarily understand what AI is – and now expect a modern , forward-thinking underwriter to do the same .

Retail is a good example of an industry that has shown consumers what AI is capable of . Research from omnichannel marketers Emarsys found that more than two-thirds ( 70 %) of UK shoppers want personalised recommendations – even though less than half ( 41 %) specifically believe that AI was positively impacting their experiences . In other words , they might not understand what AI can deliver , but they definitely want those associated benefits !

This puts pressure on insurers to explore a ‘ data transformation ’, not just a digital one . If their current systems are holding back customer sentiment , it ’ ll have to be addressed sooner or later .

But that reality comes with a caveat ; you can ’ t automate everything across the board .

Customers might not want humans at the fore of every decision , but they do expect to be able to rely on a human counterpart when required – from customer service to consultancy . McKinsey cites ‘ employee knowledge and professionalism ’, and ‘ employee courtesy ’ amongst the key qualities driving customer satisfaction .

In essence , underwriters need to harness automation and artificial intelligence where possible , without compromising that human touch . So how do they find that balance ?

Differentiation through data

Well , underwriting is about quantifying risk first and foremost . The more relevant and high-value the data available , the better understood the risks for a specific customer can be .

This makes data capture and accessibility fundamental . Research from PwC suggests that being able to ‘ better leverage business data ’ is a driver for www . intelligentfin . tech

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