The Insurance industry is coming together to adapt InsurTech for targeting sustainable growth. The length and breadth of the insurance incumbents and even the customers are realigning themselves with the technological disruption in the ecosystem. As the pace of change is increasing rapidly, insurers have balanced technology disruption in insurance processes, yet they are exploring what is next or if further innovation in insurance technology is a preferable model to leverage profits or not.
The arguments for the above question are numerous but the answers are facts, not arguments. And as a fact, a survey confirms that almost 60 percent of the global insurance C-suite executives believe that insurtechs are already driving innovation across the insurance industry. Moreover, 86% of insurers believe they must innovate with the recent digital upsurge at an increasingly rapid pace simply to survive in the competitive market.
Adaptive Nature of the InsurTech
As insurance technology is booming with new opportunities, the customers are getting used to the developments in digitalization. But the challenge now is adapting a human tone to the nature of technology to make it even more comfortable for all. Insurance technology is most recently based on the essentiality of collaboration. The insurtech companies need to engage with insurers to know their customer requirement in recent times. The new-gen technology of artificial intelligence has gone from mimicking the cognitive functions of humans to learning the abilities of the human brain through decomposition and inference to predict risks. Insurance products take the narrative of human responsiveness to their algorithms and innovate it to their convenience. For example- The reactive model of AI-ML has been transitioned into an analytical model that has advanced intelligent decision-making from earlier transaction processing.
A case in Point: Lemonade Insurance Company, an American property, and casualty insurance company is flipping the insurance business model by instantly crafting perfect insurance and promising zero paperwork leveraging bots and machine learning technology.
Lowering the Amount of Risks
Besides maintaining the efficiency and timely adequacy of InsurTech, the next step to the technology elevation is taking up the heavy and tricky task of underwriting. As is witnessed recently, the manual process of underwriting is being automated and supported by a combination of machine and deep learning models built within the technology stack. This huge innovation has changed the way risk management is handled and more than the predictive analysis of the data, technology is assuring accuracy in its analysis to support underwriting. This strategy would soon be adopted by all the insurance incumbents, big or small to revolutionize the process of underwriting.
Strengthening Data in the Deeply Rooted Insurance Processes
The value-addition of data in the insurance industry has led to making decisions mostly in data centrality. This is quite visible in the policy life cycle by identifying, quantifying, placing, and finally, managing risk predicated based on the volume and quality of data. As AI-ML has started taking command in insurance data analysis, it is imperative for carriers to develop a well-structured and actionable strategy for both internal and external data respectively. While the internal data is managed by the system, external data is imbibing to a multifaceted procurement strategy that includes the direct acquisition of data assets and providers, licensing of data sources, use of data APIs, and partnerships with data brokers.
Conclusion – The Insurer of the Future
As the recent pandemic has bridged the gap between technology and insurance players to a greater level, the disruption is still to evolve in its full power. The adaptation to new heights of insurance technology would not be just an edge in the competition but a way of survival for insurance businesses in the years to come. Those carriers that make the most out of this evolution would be the leaders of the insurance industry by creating innovative products, harnessing cognitive learning insights from new data sources, lowering costs, and convincing customer loyalty by pertaining dynamic adaptation.