Insurers have been working with big data and AI on a regular basis for some time now. These organizations fully understand that data analytics in the insurance sector is simply indispensable. Thanks to big data analytics, underwriters can assess risk, improve pricing processes, and offer more tailored products. In this article, we are going to take a look at how modern insurance companies use big data in insurance.
According to Forbes, 54% of financial services organizations with 5,000+ employees have adopted AI. The same applies to big data analytics. In this article, we are going to take a closer look at how data analytics in the insurance sector help transform insurance companies. Let’s get right to it!
Big data in insurance: Improved product design, pricing, and marketing
First off, we should mention that data analytics in the insurance sector helps in the broadly understood sales and marketing area. That’s possible thanks to all the big data underwriters collect about their customers. And, of course, the more data you possess, the more information you can squeeze out of it. In fact, insurers know almost everything about their customers–from their places of living and cars through travel destinations up to expenses. And be in no doubt–this knowledge is not gathered for charity.
Underwriters use big data in insurance to make more tailored products and services. And with the social media revolution, they now have access to new targeting options, too, enabling them to reach intended customers with specific tailor-made products that match their needs. Moreover, thanks to something called predictive analytics, insurance companies can analyze customer behavior and habits and, based on that, come up with new offers tailored to what a specific customer will most likely need in the near future.
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What we’re talking about here is already a standard solution in so-called insurtech companies, such as Hippo and Lemonade. Insurtech companies know how to make the most of data-driven technology. Every insurance product, every service, and assistance is adjusted to customers’ current needs. Lemonade has gone even further, and they’ve made a smart assistant that helps customers buy a policy online without the need to contact a human consultant. No paperwork, no waiting for someone to pick up your call. Everything happens within the app. Quick and efficient!
Risk and claim management
By far, that’s the most important part of work in every insurance company. When it comes to risk and claim management, everything starts with profiling. Each new customer has to be examined as thoroughly as possible to streamline the evaluation process. As a result, underwriters can usually provide their customers with accurate premium calculations within just a few minutes, and the whole process happens entirely online.
That knowledge about customers that we’ve just talked about is also used to assess risk more accurately. As a result, customers can enjoy correctly calculated rates which reflect their habits and requirements. Moreover, reliable customers who are unlikely to cause any problems can be rewarded with lower premiums.
In many instances, insurtech companies use big data in insurance in order to gather all the available information concerning a specific customer or property. Frequently, providing the insurer with the address is sufficient! Insurers analyze data from all the available sources to assess the risk related to this specific person/location and provide them with an accurate quote. For example, in the Hippo insurance app, you have to wait just 60 seconds to get a precise quote adjusted to your particular situation.
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So far, we’ve covered risk management; what about claims management? It’s equally as important since underwriters have to deal with tens and hundreds of damage reports monthly. In such a dynamic environment, claims analysis and management are becoming paramount. Again, data analytics in the insurance sector comes to the rescue! First off, insurers use big data in order to prioritize claims. For instance, they can decide to deal with the most complex and urgent ones first and put less important ones on hold. This way, customers who need immediate assistance can be served first.
And here’s another scenario–you can decide to deal with all the simple and undisputed claims first so that they are done and closed quickly. With big data in insurance, that’s possible as well! In fact, you can prioritize any case you want. The important thing is to remember about improving customer experience at the same time. Prioritizing “wrong” cases can result in a serious PR crisis.