TECHNOLOGY
Morgenthaler says that as other sectors began to demonstrate clear savings from using automation , insurance firms quickly recognised that they needed to play catchup . “ In addition automation uptake has been driven by ever-increasing customer expectations regarding transparency and response times , making it vital for firms who want to stay competitive ,” he points out .
But it ’ s not just about ‘ keeping up with the Jones ’ s ’. The COVID-19 pandemic has also been a massive driver in terms of transformational technology in the insurance sector . And this has been driven by necessity , says Christian Nentwich , CEO of Duco , a global provider of cloud based data reconciliation and automation .
“ There ’ s been a much stronger push for automation since recent changes with IFRS 17 and the Solvency 2 EU directive ,” he says , referring to the latest IFRS standard for insurance contracts that will replace IFRS 4 on 1 January 2022 .
“ Add to that pressure from insurtechs and the difficulty of running manual processes with Covid lockdowns , and urgency has been increasing ,” Nentwich explains .
But policy and EU regulations aside , the insurance business is a complicated one – and that means the technology to automate its processes effectively , have only recently become a reality , says Mark Colonnese ,
“Automation uptake has been driven by ever increasing customer expectations ”
PAUL MORGENTHALER , COMMERZVENTURES
Paul
Morgenthaler
Director of Aquarium Software , a technology platform provider for the pet and travel insurance industry .
“ Historically , a lot of insurance technology used to be bespoke to each insurance company , created , in some cases , decades ago when technology was very different . With the availability of on-demand computing and storage , the flexibility to do more automation in insurance has grown ,” Colonnese remarks .
He explains that as more automation has been possible , technology vendors have invested heavily in the insurance sector , so the solutions now available are at sufficient maturity and adaptability to have a wide range of applications .
“ In the early days , AI / ML-based automation platforms required deep knowledge and results were mixed . Now , technology
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