October, 20209 valuation bases (relative to best estimate claims costs) implies an almost guaranteed stream of future profits as these in-force books mature. Insurtech solutions, on the other hand, have generally been untested, with future profitability unknown and difficult to quantify relative to the required upfront (resource and cash) investments.Secondly, insurers need to critically evaluate their understanding of the data available and how they use it. Standard paper-based application forms and underwriting exams collect at least 150 data fields and could be a source of very rich data useful for creating deep insights into customers with the help of predictive analytics teams. Thus far, focus for using data has been mainly to ensure insurers follow risk management guidelines at underwriting and determine whether a client should get standard or substandard rates. After that, data is generally discarded. Developing truly customized product solutions based on data-led customer insights requires a complete change in the way we view our data as an asset. It may also require significant investment in the digitization of historical data. Augmenting existing data with additional sources from third-party data platforms provides significant scope for creating a more holistic view of customers, in terms of both understanding the true risk they pose and designing appropriate engagement processes. However, few companies are embracing external data, mostly due to concerns around data protection legislation, costs, and compatibility with existing underwriting processes and systems.Finally, the insurance industry should recognize that distribution methods have largely remained unchanged over the last few decades. Although some companies are offering online sales, this sector has not grown as quickly as expected, and most still rely on a face-to-face sales channel. For new products to be successful, they need to appeal to the sales force, be easy to explain and contain as little potential customization as possible. The result is a static product development approach with features skewed towards advisers' reality rather than individually customized, customer-insights driven solutions. Delivering solutions appropriate to today's customer requires a complete re-think of existing product development practices. New solutions are likely to require an ability to fully customize benefits, underwriting processes, sales journeys, and post-sale engagement. New product sets require expansion beyond the single, one-size fits all approach of today to a holistic range of potential solutions, offered to and customized for customers based on what we know about them and what they need.Interesting technologies like OCR, data aggregation and analytics, dynamic underwriting engines, and AI-based engagement platforms are becoming more mature and will no doubt play some part in this journey. However, the transformation challenge will likely become a mindset change challenge rather than an Insurtech adoption challenge. GIVEN THE AMOUNT OF INSURTECH ACTIVITY, IT'S ONLY A MATTER OF TIME BEFORE THERE IS A LARGE-SCALE EMBEDDING OF NEW TECHNOLOGIES IN THE SECTOR, AND ONCE IMPLEMENTED, RAPID ADOPTION IS LIKELY TO FOLLOW
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