Q1 Insurance Data Has Unveiled Interesting Trends When It Comes To Policy Recommendations
Artificial Intelligence (AI) is being used in many more industries now, and the insurance sector is no exception. In fact, it’s being used to rapidly analyse people's data to determine where they may be lacking adequate coverage, to reevaluate risk assessment, and to ensure people have the best policy premiums possible, based on their data: not the data compiled and averaged from thousands of people globally. AI is giving insurers the unique ability to create hyper-personalised suggestions for people in a way that was not previously available.
Insurance technology company Zelros is using AI in exactly this way to analyse data on consumer behaviours to make hyper-personalised recommendations. In 2021, they had coverage for up to 250 million policyholders globally and were able to determine where people were lacking insurance coverage. The data enabled them to make 10 million personalised insurance recommendations to individuals and families in 2021. This data provides a glimpse into the economy and what areas of people’s lives are changing the most. Now in 2022, the recommendations for Q1 are providing insight into what’s changing now:
JAN | FEB | MARCH
Car | 2.86% | 0.10% | 0.03%
Credit Insurance | 19.95% | 19.92% | 19.07%
Health | 14.95% | 13.99% | 14.11%
Home | 30.42% | 32.42% | 32.33%
Legal Protection | 7.15% | 6.03% | 6.11%
Life Accident | 24.61% | 28.38% | 28.31%
Term Life | 0.07% | 0.06% | 0.04%
With the data on these policy recommendations spanning over the first three months of 2022, it’s interesting to note the value drops and gains of what is being recommended and when. For example, for motor vehicle coverage, it has continued to fall month after month starting at 2.86% of recommendations in January to only .03% in March. Meanwhile, credit insurance, health and legal protection all dropped for the month of February but have slightly risen for the month of March. Recommendations for home insurance rose exactly 2% from the first month of the year to the second.
Overall, when looking at the data provided for Q1, the changes from February to March aren’t nearly as drastic as those from January to February. The top three categories are credit insurance, home and life accident. Together, these three are making up roughly 80% of all insurance recommendations. As we’re now into Q2, it’ll be interesting to see how the data flows for the first six months of the year, and then to compare it with the end of the year results once we hit Q3 and Q4.
How does hyper-personalised insurance work with AI?
When we get our insurance, we often think that what we’re getting is hyper-personalised to us, but this is not usually true. Historically, agents are trained to cover the bases of what the average person needs, but this has nothing to do with your specific current needs. They can ask questions and make a personalised recommendation based on what you tell them, but hyper-personalisation through AI takes this to the next level.
Hyper-personalised insurance uses artificial intelligence to make specific recommendations to a policyholder based on what is happening in their life right now. With roughly 2.5 quintillion bytes of data being created every single day, a portion of that information is valuable consumer information that is being used to teach AI how to draw conclusions about what people need and want before the thought even crosses their minds. This data can be analysed to help an insurance agent determine if their client (aka you) has had a major life event that would signal a need for new insurance coverage or the re-evaluation of an existing policy. For example, AI could help determine if you’ve moved to a new address and would need to revisit your property insurance coverage to ensure your risk category is still the same and your policy still covers you. It could be used to show the birth of a child, which would prompt an agent to ask specific questions about this to see if life insurance is now needed.
Hyper-personalisation can also include telematics data that makes your policy specific to just you: for example, an auto policy based on your unique driving history. Cars are now so technologically advanced that insurers can provide behaviour-based insurance, so your rates become based on your driving behaviours only, not the pool of driver data used to determine a standard policy.
Why hyper-personalisation is important
- Better customer experience
- Best policy premiums possible
- Proactive outreach from insurance agents and having smarter conversations
The leveraging of AI and machine learning to meet our needs is a reality now in the insurance industry. It’s helping recommend to those without adequate coverage, to reevaluate risk assessment, and to help make sure that policyholders have the best premiums possible. AI is giving insurers the unique ability to create hyper-personalised suggestions for people in a way that has never been seen before. It’s best that the entire industry jump on now, or face being left behind and never catching up.
About the author: Paul-Henri Chabrol is Chief Product Officer at Zelros.