AI
Machine Learning algorithms can be trained to discover policyholders ’ behaviour , preference , the inclination for risk , and even fraud , learning from past examples and adapting to new ones without the need for human intervention .
a result , insurers have assessed customer risk profiles and can reduce the number of questions asked to the applicant , significantly removing friction .
Brian Mullins : Current data volumes mean that processing quotes , renewals , claims , customer requests , etc ., is at the limits of what can be handled by human operators assisted by traditional information systems . In the past decade , companies have turned to robotic process automation to speed up manual processes and amplify the work of their handlers . Yet today , this is still not sufficient , and to survive in these oceans of data , let alone to innovate and stay ahead of the curve , insurers like many other businesses have turned to AI to discover more subtle patterns in data , automatically adjusting to new trends , threats and opportunities .
There is a natural suspicion of AI that it will result in fewer jobs for actual humans as manual roles become less available . How does this play out in the insurance sector , and do you think it ' s true ? Wayne Butterfield : To date , and very likely , even in the future , automation hasn ’ t actually caused any significant job losses . Instead , we ’ ve seen and will likely continue to see it focus on task automation - many other activities still need to be completed by a person . I ’ d estimate around 90 % of efficiencies gained so far have been used to negate the backfilling of open positions or dealing with extra volumes in other parts of the business , which is why there has been very little in the news ever about major job losses to date .
Paul Donnelly : We are not seeing a reduction in demand for skilled underwriters . In fact , in many markets , the opposite is true . Take the example I gave of our UK client that deployed our SPECTRA system as a pre-sales tool for advisers to get an indicative underwriting decision . This function was previously performed by members of the underwriting team .
By making this change , the insurer was able to improve the service to advisers , not only by providing them with immediate dayor-night access to the likely underwriting decision for their client , but also by diverting the resources previously assigned to this task they were able to commit to making underwriting decisions within one working day , with decisions available Monday
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