The life insurance industry has become a little slower to adapt to using big data over the last few years. Yet, big data for life insurance has expectations to change the industry forever once they start adopting the right platforms. They can take the lead from another insurance industry that managed to get into big data first. In the property and casualty insurance industry, they’ve already taken on big data to better analyze risk selection.
In life insurance, big data awaits for use in similar ways. However, we’re likely going to see it used in a number of different ways based on how complex life insurance is.
For those who wonder when life insurance is going to start using big data, it’s now. While they’ve started using the basics, they still have a way to go to get to full use. It’s not to say they haven’t planned out what they intend to use big data for in the near future.
Let’s take a look at what many life insurance companies say they plan to do over the next two years.
What Are the Top Strategic Goals?
In a Willis Towers Watson survey, many life insurers said they first plan to use big data to improve risk selection. More precisely, 50% of those surveyed said they’d do this, which tells you how important it is in the life insurance industry.
Giving better insight into who’s most at risk can help life insurance companies set more competitive premium rates and weed out those costing them more money. Before the age of big data, far too many insurance companies had to do insurance underwriting through a more time-consuming vetting process.
Determining who was at risk usually meant months of research on medical history, or inspection reports. Big data assembles all of this information in one centralized place and with immediate insights, saving weeks of protracted research.
As an addendum to risk selection, the above survey showed 25% of insurers want to use big data to target profitable customers. Using more data to assess risk helps scope out clients the insurer knows has a better chance of living longer.
Using Big Data to Improve Customer Relationships
While the above goals show that life insurance companies need to look out for themselves, it doesn’t mean they don’t want to improve relationships with their clients.
In the survey, it’s noted insurers want to use predictive analytics in big data to expand customer relationships over the next two years. Similarly, they want to enhance customer value proposition over the long-term.
It proves how symbiotic the life insurance industry is, and big data can make this happen without clients even knowing their insurers know more. Equally important, though, is using big data to transform business models and improve internal performance management.
At the heart of this is data mining, which is a key element in using big data successfully.
How Data Mining Works
With further evidence showing insurers want to do data mining to better understand their clients, what sources do these companies need to use to find thorough information? Through the Willis Towers Watson survey, it’s shown administrative systems and claims data is where most of the big data comes from internally.
On an external basis, medical records and prescription data are the biggest data mining sources. Putting these together can obviously tell a lot about a person’s health record. Nevertheless, insurers plan to eventually use other sources like email, credit scores, and social media to extract data.
Big data is going to extract voluminous data from every online source imaginable to give a definitive portrait of people who depend on life insurance.
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