Historically, the most difficult part of correctly determining the pricing on an Insurance Policy, is in determining the various Ratings for both the primary policy and any additional riders (other specific cases) attached to the policy. This is compounded by the fact that even the base policy, which is often determined by a class of related individuals who share similar characteristics, may contain 10’s of different “known” factors, as well as many “unknown” factors.
While it is not a perfect solution for quickly determining the Rating on either the base policy or any riders, the application of Machine Learning / Artificial Intelligence (AI) can more quickly predict patterns, and thus the risk level for a given Insurance Policy. In this article we will discuss areas where AI is ripe for usage within the Insurance Industry, as well as changes in the overall industry that are harder to accurately predict.
When determining the Rating of a Base Auto Insurance Policy involves many different factors. Some examples include the following, along with questions about modern social and technical developments.
Marital Status (1)
Distance Driven per Year (2)
Zip Code / Postal Code (3)
Primary Parking Location (4)
Environmental & Weather Effects
Primary Purpose of Vehicle (2)
Primary Driver’s History (1)
Color of Vehicle (1)
As well as many more.
(1) Naturally, one of the biggest technical impacts during the next 10+ years is going to be the continual development and adoption of “Self-Driving” vehicles. As we start achieving Self-Driving vehicles of level 3 and higher, how important will some of these factors play into determining a policy rating. For example, if the vehicle primarily drives itself, then is it really important that the policy is for a Male, 23 years old, and Single?
At the same time, what happens when:
(2) Several Insurance companies already have devices that can be attached to the vehicle to determine speed, quickness of starts / stops, distance, and other diagnostics of the vehicle. Now take this a step further, can the vehicle report this information and even the specific location to the Insurer when connected to either a satellite or cell phone tower? Delivery companies already have some of these capabilities, but the Insurer could use this information to determine if the vehicle is speeding, making high speed turns, etc. which could affect the risk profile for the policy.
Likewise, does a person have to select an option describing what their trip involves, and can this information also be transferred back to the Insurer?
(3) Today it is easy to determine the Zip or Postal Code where the vehicle is normally kept, because it is part of the address information. But what if we could start getting more granular - vehicles within X miles or Km’s from the exact address location?
(4) Today this is difficult to capture with any degree of confidence. However, what happens when the vehicle is able to quickly take a 360 degree view of the surroundings of the vehicle when it stops and it is at its primary “Home” location? Then apply Artificial Intelligence to this.
As reported by Reuters. In 2007, Lee Romanov and her team from InsuranceHotline.com did a study, just for fun, of 100,000 Drivers in North America looking at 6 years worth of the Driver’s record and compared this to their Astrological (Sun) Sign. And the findings actually shocked Lee and her team. It ended up that the Driver’s Astrological Sign is a larger factor than Zip / Postal Code or even the Age of the Driver.
Worst Drivers: Aries, Libra and Aquarius
Best Drivers: Leo, Gemini, Cancer, and Scorpio
One we start collecting all of this data from the Vehicle that is being insured as well as others, either from other Insurers or from other covered vehicles within our portfolio; then we can start detecting patterns. And once we know these patterns and the correlation coefficient between various patterns, we can potentially quickly adjust the Rating of the policy.
For example, let’s take the Color of the Vehicle*:
*These are examples for demonstration purposes only.
And you could easily add additional Factors to this analysis, like the Manufacturer, Model, or even Zip / Postal Code, etc. in order to determine what combination of Factors is the best at determining what the rating for a given Base Policy should be.
At the same time, one must be careful of having too many factors used in a given calculation, as you have to use some weighting factor based on the data. This is another area where AI can assist, because it can quickly determine which combination of factors have the greatest impact on a given Policy Rating. For example, a 30 year old male, wants to insure his brand new Mercedes SL Roadster, which is red, he lives in Scottsdale, Arizona 85054, and has three speeding tickets within the past 6 years, etc.
Using this information now AI can compare this to other individuals within the same Zip / Postal Code, age range, similar type of vehicle, etc. in order to determine which factors have the greatest impact on the overall Policy Rating.
By doing this, AI could determine a unique Policy Rating for a given customer, after looking at all of the data and information. Naturally, this is not going to happen all at once and in fact will need to be constantly tweaked and modified as new information comes available.
The same concept of using AI to look at the various data points and determine a Policy Rating can be applied to most but not all other types of Insurance.
The traditional factors, like age, gender, smoker, overweight, cardiac issues, diabetic, length of coverage, amount of coverage, etc. would certainly still be considered as primary factors when determining a Policy Rating.
But as technology continues to advance, what happens when:
The same is true when an Insurance company is determining the Policy Rating for a house, piece of property, factory, commercial store, etc.
Here again technology, along with environmental changes will have potentially dramatic long term effects on the Policy Rating for a given property:
There will always be some items that AI won’t be able to determine an accurate Policy Rating, because the item or potential damage to it is very unique, and there is not enough similar data to determine what the overall risk is of damage, loss, or injury to others. However, we would expect that over time these unique items become fewer and fewer.
Artificial Intelligence (AI) and Predictive Analysis, in conjunction with new Technology and Environmental Impacts, will have a very significant impact on how Insurance Companies determine the Policy Rating for a given insured item, property, or person.
This change won’t happen overnight, but will represent an evolutionary advancement in how Policy Ratings are done. Some will replace standard actuary tables, as AI becomes better at accurately predicting the risks for insuring something. It will be interesting to see how the various Regulatory Bodies make adjustments to take into account all of these things - from technology advancements to more accurate Policy Ratings.
Naturally, Insurance Companies who stick with the traditional Policy Rating models, will over time either be outsold by more accurate policies that use AI analysis; or worse take on more risks than they should, thus increasing the risk to the overall business as an ongoing corporation.
And if you don’t think this can potentially happen even with AAA rated insurers, one only needs to look back to 2008, when the massive Global Insurance Company AIG nearly failed and had to be bailed out. Otherwise, there would have been a cascading failure among many of the largest banks and financial institutions globally.
We hope that you have enjoyed this article on how AI might be applied to Insurers and Policy Ratings, today and in the near future. To learn more about our AI solutions click here.