Making Blockchain Technology In Life Insurance Work For You
June 19, 2019 Leave a comment
Back in April 2018, when I read the press release from Cognizant about having teamed up with a consortium of fifteen Indian life insurers and developed a blockchain solution to facilitate cross-company data-sharing to help these life insurers reduce their reliance on data intermediaries and aggregators in obtaining customer and policy details for a wide range of critical purposes, such as know-your-customer due diligence, financial and medical underwriting, risk assessment, fraud detection, and regulatory compliance, I was FURIOUS.
Why?
You see, I am a long-standing customer of 2 of these 15 life insurance companies. One of them was the company I made my very first purchase from all those years ago, on the recommendation of a banker I trusted. And at that time I had in all honesty made full disclosure of all the information they sought in their proposal forms and the additional queries their underwriters had. However, I made the disclosures for the limited purpose of purchasing specific policies from these companies. And at a time that block-chain was nowhere in anybody’s mind space. Read more of this post
Blockchain is a shared, distributed ledger with non-repudiation of transactions that can work in the absence of trust across a peer-to-peer network. Blockchain technology has the capacity to deliver wide-ranging benefits to humanity that are obfuscated by the charged debates on cryptocurrencies these days. Applied to insurance,blockchain technology opens the door to many positive developments for consumers,such as: 
Currently the health and life insurance products we buy are static in the sense that right at the beginning of the policy term the insurance company makes an assessment of the morbidity or mortality risk of the person and then agrees to insure them at agreed-upon rates.
Predictive analytics involves the analysis of large data sets ie big data ,to make inferences by identifying meaningful relationships between different variables and using these relationships to forecast what might happen in the future with an acceptable level of reliability.Predictive Analytics includes what-if scenarios and risk assessments.
The mortality risk is not the same across different sections of the population.So one of the ways in which life insurance companies have traditionally competed is by restricting their offerings to people who have lower risks of dying early and thus keeping premiums lower and/or bonuses higher.The classic example is the Postal Life Insurance plans of the past that were offered exclusively to government servants vs similar plans of the LIC that were open to all. In more recent times,insurance companies target the more educated, affluent, urban ,financially successful professional/managerial class with better living standards and access to world-class healthcare.
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