Risk Personalization in Auto Insurance – The Rise of the Machines

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24 September 2020


What if, suddenly, everyone stopped driving?

COVID 19 just asked an insurance question that has never been posed before.

This is the greatest global economic event of our lifetime. The COVID-19 inspired dramatic shift to work from home, massive unemployment, and “bubble living” has shrunk mobility in almost every dimension.

“Yard sculptures,” formerly known as commuter cars, incur payments, taxes, fees, and insurance, while actively depreciating and delivering no value to households. For households with no work or no place to go, the opportunity to drive is looking expensive.

“The end of an old way of working is beginning.”

A shift in mindset around monthly expenses happens in every recession. This one is different in two ways. One, the sheer scale of economic impact is heading towards the worst slump since the Great Depression. Two, every part of analytics and digital connectedness is at an inflection point of dynamic improvement — data, AI, and cloud — are all accelerating simultaneously at a pace beyond historical highs.

How long will this last? How many people won’t pay their bills? Who will forfeit their insurance or forego proper coverage to reduce expenses? Who will argue for usage-based refunding? Who will shop for better price-to-risk value? What will companies do with this transitory opportunity?

This report describes a convergent fusion of three major trends:

  1. Scaling observational data to the connected world;

  2. Advanced analytics for risk-based pricing of mobility; and,

  3. Decision support for personalization of risk.

    The impact of data, AI, and cloud is creating a thematic backdrop for everything being part of the “science of better” thinking — better data, better price, better experience, better transparency, better cost. In market now (or coming soon) on your mobile smartphones and connected cars. Machines talking to machines with permission makes insurance easier, faster, cheaper, and better.