The Queen’s Gambit and Auto Insurance?
By: Motion Insurance
Tags: Autonomous Vehicles, Auto Insurance, AI, Connected Cars

By Lanaya Nelson

If you haven’t heard of The Queen’s Gambit, I suggest you give it a watch. It is a real treat for the mind and can bring about an entertaining reprieve during these…still…difficult times. All escape-in-entertainment aside, what does the Netflix show have to do with insurance? Let’s explore. (The Queen’s Gambit is about a female chess champ… and is also a strategy in the game of chess.)

Let’s start with AI

We all know artificial intelligence is here, and it’s not going anywhere, so might as well cozy up and get better acquainted. Remember AlphaZero? If you’re not a chess fan and/nor a techy, basically Google developed algorithms for a computer to beat expert human chess players in the game of it. Big deal? Kind of, and also, kind of not.

The take-aways from AlphaZero are 1) it is fascinating that a computer can interact and, in near real-time or real-time, respond with the best ‘choice’ for a current ‘state’ and 2) that in a game of chess, the possibilities/algorithms are binary, set, and logical: if this, then, that. This works great to define a golden standard or for benchmarking. Unfortunately or fortunately, the world is not a chess game.

Regarding the best ‘choice’, we will return to this in next week’s blog in ‘Defining the Win’.

Act of God

The ‘kind of not’ stems from the fact that the world is not made up of binary states and events that can be predicted and/or explained, and human behavior is certainly not binarily predictable (yet). The world is full of unknowns, illogical behavior, inexplicable happenings, and anomalies. AlphaZero only works with ‘classical games’- games in which all players have perfect and inclusive information available to them. AlphaZero does not work with games like Rock-Paper-Scissors or most card games in which there are states of randomness and unknowns/hidden states. (Recent advancements will be discussed in the next blog.)

But, with every bit of additional information/data point, algorithms move closer to having perfect information, and are, by logic, closer to predicting the best move for a certain state.

Back to the Point: Chess

Chess is a game of strategy, opportunity, and calculated risk. It’s all about putting yourself in the best position on the board while trying to predict and anticipate what your opponent is going to do next. Kinda like driving and the risks of driving. There are fixed rules, but every player/driver comes to the board/road with varying sets of skills, years of experience, habits, and behaviors- and we cannot forget the environmental/external risks of the game/road that can change states and the trajectory of them in an instant.

Ok, great, so where does insurance come in?

Auto insurance companies have been doing this (selling auto insurance) for a long time. I mean a lonnnnng time. The first U.S. auto insurance policy was written in 1898 (soon to be 123 years ago) by Travelers Insurance Company to a doctor in Buffalo, NY- a $5,000 liability coverage policy for $12.25. Auto insurance is no spring chicken.

We are upon an historical inflection point (isn’t it cool to bear witness?) and up to this time, auto insurance risk has been calculated similarly to how one might calculate the risk of a player in a chess game- based on factors in which you can prove your logic on laws of averages. Try it! If you were going to bet on/insure a chess player in a game that has a $1000 pot/pool, what are the factors that you would want to know to best place your bet/price your insurance for that player? (Without the use of AI/machine learning/algorithms/historical data/predictive models… just basic “facts”/judgments on this player.)

Whatever your answers, I bet they are not too far off from the ones used currently in auto insurance rating/pricing. And that is just one game and one player. You can see how, even if the insurance industry started with simple factors, like the ones that may have first come to mind, actuarial science (how insurance mathematically and statistically measures risk) models have evolved to be quite complex, and certainly nothing to downplay or take for granted, but still not perfect (and certainly discriminatory- as they are based on averages).

The Queen’s Gambit Returns

The game and professional organization around it actually has a pretty good system in place for predicting game outcomes and betting on them. Every chess player has a rating based on their past performance, not on their age/experience/gender/zip code/education level/etc. If you were to return to your role of betting on/insuring a player, betting on judgments of proxy data/facts alone IS the risk. If you had no way of knowing how a player actually played the game and their unique history within, you might bet higher on an older, financially successful, married (yes, that is factored in auto insurance pricing) man over a young, single woman with a high school level education, even if the chances are in the young woman’s favor because, historically, she is the much better player.

The game of chess and its possibilities of outcomes, which are many, I mean…many, are still fixed ones. No matter what. That means that the risk lies only in the player and the environment, as AlphaZero has proven. Making these calculated judgments (how auto insurance risk is currently measured) on a person has better odds than betting on randomness alone or on a game of heads or tails. With the technology we have now, we can do better.

“Today in auto insurance, due to how the industry has treated legacy data and approached data engineering, we don’t even know what a “right” move looks like yet. In fact, what we don’t know in regards to driving behavior, environmental factors, and accidents is likely significantly greater than what we do know.” -Daniel Weisman, Co-founder and Chief Innovation Officer of Motion Insurance.

To Daniel’s point, we need to acknowledge that there is a huge gap in my metaphor of chess and auto insurance risk. In chess, there is a winner, a loser, or there is a draw. In auto insurance risk, the focus is on the outlier. There are terrible drivers that will never cause a crash and there are phenomenally safe and good drivers that will crash. We will work on ‘Defining the Win’ in next week’s blog.

Both of these things can be true… a little Taylor Swift logic

The risks of the road and anomalies of human behavior are endless, but there is a good chunk of driving and road risk that can be benchmarked using advanced technology and AI/ML/algorithms. Why not utilize what we’ve learned from AlphaZero’s winnings to reach a more sound and predictable understanding of what it takes to be on the winning side of driving and road risk? Not to mention, the winning side of this risk means saving lives… not just turning a profit.

Truth 1: The binary truths of driving and road risk can be utilized in near real time or real time models to help prevent crashes, loss, injury, and death.

Truth 2: There are endless non-binary possibilities that still need to be accounted for.

Auto insurance risk is a combination between a chess game, ‘Go-Fish’, and Russian Roulette. It is much more likely that there will not be a crash, and the likelihood of a crash or the avoidance of one cannot be 100% predicted.

Stay tuned for next week’s blog, ‘Defining the Win’.