Understanding The Analytics Of Insurance

understanding insurance analytics

On the customer end, the way insurance companies handle their premiums and payouts can feel arbitrary. Of course, in reality, this is far from the case. Insurance companies' decisions are made with two goals — minimize risk and maximize sales. 

To strike this delicate balance, insurers need help. While data can’t predict the future, it can identify patterns that will predictably continue to play out going forward. In this article, we take a look at how insurance companies use analytics to handle their business. Read on to learn more about insurance analytics information for the insurer and consumer. 

Predictive Analytics 

Predictive analytics are designed to take some of the variables out of insurance coverage. Essentially, they help insurance companies set their premiums by calculating how risky a specific customer is going to be to place coverage for. 

All forms of insurance use this data application model. The riskier the customer is seen to be, the higher the premiums they will pay. 

Risk Analysis 

Insurance, at its core, is about analyzing and planning for risks. Insurance companies face the potential for large payouts every single day. How do they go about providing enormous amounts of coverage and still sleep at night without the fear that a train derailment or storm will force them into bankruptcy? 

Data 

Of course, insurance companies have always used analytics to analyze risk. Now, modern technology simply allows them to do it at a much higher level. If they want to place rail coverage for a significant railroad, they can look at the business's historical safety records. They can also analyze predictive weather patterns for the path of the railroad. Maybe there hasn’t been a long history of bad weather on this route, but isn’t it true that the last two years have seen higher water levels? 

Weather is a particularly good example because we are now moving into a time when climate change is having an increasingly high impact on rain levels, flooding, and lightning storms — all factors that can lead to insurance claims. 

Amongst all the uncertainty, data can shed some light, making it easier for insurance companies to generate a risk management advisory and take calculated risks. 

What Data Isn’t 

Of course, it is important to recognize that for all good data can do, there are limitations. It is not a crystal ball that allows its users to look into the future. What data does is look into the past, recognize patterns therein, and project them out into the future. 

The results you get from this technique will inevitably be imperfect. For insurance companies, that is ok. Their goal isn’t to receive a play-by-play of everything that will happen in the lives of their policyholders. All they need is balance. Enough low-maintenance accounts to balance out the higher claims, and still allow the company to turn a profit. 

For data, that is a perfect formula. The numbers don’t need to get everything right. Just enough to keep the wheel turning. And of course, it benefits policyholders as well, allowing insurance companies to set competitive rates while still turning a profit. 

Product Development 

Of course, data isn’t just there to help businesses understand risk. Insurance companies are ultimately selling a product. It’s not tangible, but what they provide is just as transactional as the goods of any other company. 

They need to develop products. Coverage plans that appeal to enough people to make them stand out amongst the competition. Data can help accomplish this by identifying what customers like in an insurance policy and tweaking their current offerings to place more coverage. 

In fact, data will ultimately be used by most if not all branches of an insurance company. Marketing will use it to tweak their messaging. Sales will use it to identify potential opportunities. People with traits X, Y, and Z are most likely to go for plan upgrades and advanced coverage. Knowing this, they can look through their customer data, and find people who fit their ideal customer profile — that’s to say customers who will be most interested in further buying opportunities. 

And because data is all about learning from the past, the efficacy of these numbers will only increase as time goes on. The algorithms will adjust based on incoming information, making it easier for businesses to make the most of their numbers in the future. 

The Big Benefit 

Theoretically, predictive analytics can help to maximize the number of people with insurance. Insurance as a concept actually works best with more users. Take ten people. One may have a major claim this calendar year. The other nine probably won’t. 

All ten of them pay the same rates, and the nine low-claims policyholders subsidize the expenses of the higher maintenance account, making insurance more affordable for everyone. 

When The Affordable Care Act was first launched, insurance companies were immediately hit by an enormous number of claims. Why? Because people who had never had insurance before were catching up on years of missed appointments. The companies had no precedence for this moment in history and were consequently unprepared to plan for premiums. Many people experienced higher costs. 

What role could data play in making a situation like this one go smoother? For one thing, it can use algorithms to get a more insightful understanding of predictive costs. How much does the average customer cost an insurance company during their first year of coverage? How many new customers will state-subsidized insurance create? And so on, since all of this needs to be considered for insurers and the insuree. 

Once the insurance company understands its own costs better, it can then make an informed decision regarding what sort of plans they need. Customers in lower-risk brackets may be extended low-cost, moderate coverage plans that will take care of them in the event of an emergency without resulting in enormous monthly payments. 

This alone won’t necessarily result in wider coverage availability. Some people won’t be able to afford coverage without legislative action that subsidizes care for them. Still, analytics are an important piece of the puzzle, providing options for the customer and much-needed security for the insurance company.

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