How much are you willing to pay for data? Waymo is the self-driving unit of Googleparent Alphabet. Waymo will be releasing a driverless ride-hailing service soon in a suburb of Phoenix. What makes this interesting is that some experts are saying that 80% of motor insurance premiums will disappear over the next decade as driverless vehicles reduce accidents drastically. Whilst reducing accidents is great, it also points to a shifting landscape, a landscape few people can predict with any level of certainty. This uncertainty is shown and magnified with the recent case in the news. Recently, an autonomous vehicle struck and killed a pedestrian. A lot has been written on this, the pedestrian was a homeless lady who stepped out from the shadows into the car. Should we blame the pedestrian? This is not really in line with current practice. Do we blame the owner of the car? Do we blame the programmer?
As actuaries, we work off of data. Give us data and we can use our modelling and predictive capabilities to manage risk. The challenge is how to collect data useful in a world with constant disruptions. In this example of the driverless vehicles of Waymo, an insurtech named Troy will be insuring the passengers of Waymo at no cost to the passengers. Troy is underwritten by an affiliate of Munich Re, who was willing to take a risk given the lack of data and claims history surrounding driverless cars.
As innovation continues not only in insurance and insuretech but in so many related industries, putting ourselves in a position to collect and understand data will be key to long term success. It is true that many companies, including insurers, are employing data scientists to mine their existing data to find competitive edges, but the bigger picture is what kinds of products and markets can we get into to collect new data and how aggressive should we be in pricing in order to get this data.
Any time we go into new market segments, it is the field of the actuary to predict, model and monitor experience to drive the success of the company (the actuarial control cycle). This goes well beyond the role of the data scientist without actuarial training and experience. So, what kinds of markets offer the opportunity to collect new data and drive success in this shifting world? Ask your actuary of course!
