Mr X, a fitness freak, aged 30, takes good care of his health. Mr Y, aged 30, hardly spends time looking after his health but gets the same premium for the same coverage amount/policy term.

There are two aspects to this, Mr X after a few years may feel that given he is healthy, the premiums do not justify the insurance benefit and there is a possibility of him dropping out from paying his renewal premium. The reason the premium is perceived as relatively high, is simply due to the other members who may not be healthy, and the same price is being charged. Secondly, if a greater number of healthy people don’t renew their policies, the insurer’s book will lack health enthusiasts, increasing the possibility of claims being higher than anticipated. These two facts show that both the insurer and insured need customized and personalized pricing. The use of technology for personalization has been spoken of for several years but with the advent of Wearables and Apps, which maintain data on health this possibility has been renewed.

Let us look at the use cases of both these lives and the scenario for personalized pricing. Initially, when both Mr X and Mr Y generate quotation, they will be quoted the same premium. After the quote, both of them submit other details that are needed by the insurer to underwrite their respective applications. In addition to the mandatory input sought by the insurer, Mr X allows his Health App to share his health data for a certain period of time, as required by the insurer. Both Mr X and Mr Y will go for their medical checkups and the medical data will be shared with the insurer.

The underwriter or the AI Model goes through the data and given that Mr X has shared more data, the AI model will refer the mortality table, provide an appropriate prediction on the longevity of Mr X. Further, it performs the actuarial calculation with other static assumptions as guided by the model to arrive at the revised premium. This revised premium will be different from the premium that had been quoted at the initial stage - This is personalized pricing.

What is more relevant for us to understand here is not only the fact that the health data was used, the AI along with the actuarial calculations were used in underwriting the policy to provide personalized pricing. With time, the model reinforces learning and at the renewal stage with more data available, there is a possibility that the renewal premium will be different from the previous year premium. Not only can the AI model be built to use the health data at the time of renewal, incentivizing the insured for maintaining better health, the model can also use the claims data to revise the assumptions to stay relevant. This way both the insurer and insured are benefitted by the technology creating a win-win situation. This can be further enhanced by using blockchain to procure data on hospital visits or medical records. The possibilities are endless here.

Without the use of technology, Mr Y, traditionally, would be provided a flat premium or an increased premium based on the inputs that were provided at the time of underwriting.

Here, technology carries a lot of scope. Will the technology change the only industry in the world where the premium (the price) is fixed in the long-term? If technology is adapted, it will create value for both the customer and the shareholder. When sports and other fields use technology to learn and improve their performance, the same reality is closer for the life insurance industry too.