At the heart of the insurance value chain, claims and underwriting are the natural place to start. In Clarity on Insurance, we look at how to modernize claims processing and underwriting approaches in order to enhance efficiency, mitigate cost pressures, improve insights and reporting, and deliver a superior customer experience.
As we look at the future shape of insurance organizations, we must reflect society and customers’ evolving expectations. For personal insurance lines, this means changing insurance from an administrative task to a lifestyle product that accompanies customers throughout their day, with automated alerts and helpful advice. For commercial insurance lines, it means proactively helping clients identify current and emerging risks. For both areas of insurance, digital solutions should leverage data, cloud and cognitive – so that insurers offer products and solutions that seamlessly cover the full range of prevention, protection and care.
Upgrading the claims organization for latest technologies would enable insurers to make better use of the data they hold, and to move to purpose-driven claims. From applying data-driven insights to prevention, using data analytics to predict likely claim litigation, and deploying sensors and drones to help recover losses – I see huge potential. What’s more, digital solutions can be applied to the entire process, before, during and after claims.
As a core process, life underwriting should take advantage of technology solutions to better understand customer interactions and reach complex decisions. Underwriters need to assess risks from multiple angles, and to analyze large and growing amounts of raw and structured data. Artificial intelligence, or AI, can give them the ability to quickly process huge volumes of data across multiple sources simultaneously.
Non-life reserving is another area that could benefit. It has been extremely slow to evolve and remains a resource-intensive manual process at many insurance organizations. Improving it requires reviewing reserving software tools, redesigning processes, and deploying AI, among other measures. As well as improving efficiency and overheads, it can lead to more in-depth analysis of key reserving risks and freeing up specialists to perform more valuable tasks and reach higher quality decisions.