Chapter 4: Exploring the TechnologyLandscape: Technology Themes andImplementationInsurance can’t make serious claims to being a truly modernised sector but it’snot so backward that it’s stuck in the technological dark ages.In fact, the UK boasts a thriving Insurtech sector withhundreds of startups operating across the country.Once tipped to completely replace the traditionalinsurance market, Insurtechs have recently settledinto a more supportive role, enabling incumbents tomodernise their operations in very speci昀椀c ways.Insurance has also been developing without theassistance of Insurtechs in its wholesale adoption ofbig data and advanced analytics. Underwriters havenever had so much clean, actionable data at theirdisposal, and they’re using it to assess risk moreaccurately and to price more e昀昀ectively than everbefore. AI, robotics, cloud computing, big data, APIsand wearables – there is hardly a piece of cuttingedge technology that insurance isn’t using.However, the operational impact that technology ishaving is debateable, as arguably insurance continuesto lag behind other 昀椀nancial services when it comesto delivering on modernisation, but it does haveone advantage over its peers – the sheer volume ofdata the sector collects on a day-to-day basis. Thatdata, when identi昀椀ed, categorised, and understoodcorrectly, is the insurance industry’s ticket to catchingup with, and perhaps even surpassing, the levels ofmodernisation seen in other 昀椀nancial services today.Fighting data with dataThe volume and availability of data in the insuranceindustry today is driving the need for modernisation;a modernisation that will be built upon even moredata. It’s a self-perpetuating cycle, one that makesit increasingly di昀케cult for any underwriter ororganisation to compete without going all-in on data.Evolution of where underwriting data has been typically heldBrainMainframe /Green ScreenPaperemail / OnlineUnderwritingWorkbenchesToday, modern underwriters use data to monitor activity in real time, connect to external sourcesto track changes to a risk pro昀椀le while keeping an eye on any emerging risks. Fuelled by good datathey are able to be much more selective, using data and insight to seek out the risks they wantin the areas they’re comfortable with. This approach has led rise to the modern day underwriterworkbench, a tool that is maturing, with many insurers starting to explore as a distinct system.Basic1LEVELManagedLEVEL 2ManagedLEVEL 3ManagedLEVEL 4OptimisedLEVEL 5InitialDocumentingestion usingPL:P to extractdataDevelopingSubmissions昀椀ltered usingcontext orsentiment analysisConnectedSubmissionsconnected into aworkflowMeasuredSubmissions areable to be scoredand prioritisedOptimisedUnderwritingorchestrationincluding pricing,risk appetite andpropensity tocloseFigure 4.1: Evolution of underwriting data stores24
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