Big Data

Big Data. Another buzz word that’s been dancing on the tips of our tongues for well over a couple of years now. The trendy topic dominated the insurance industry, creating a whole army of Big Data evangelists.

With the increasing amount of data now available at our fingertips, and the potential for collecting and storing such huge amounts of data, insurers are able to use advanced analytical tools to build more accurate predictive and statistical models in order to determine the levels of risk for different insurance customers. By doing so, insurers will effectively be able to reduce the cost of premiums for their customers whilst also ensuring themselves better profits based on more accurate risk analysis.

Those who pose greater risks can be charged a premium that will cover those risks, and those who pose less of a risk can be charged less. This is a far fairer model than simply basing premium prices on demographics, taking into account individual circumstances. For example, drivers’ auto insurance premiums can be based on their personal driving activity and health insurance can be charged based on the customer’s fitness and activity monitors such as the popular wearable devices, Fitbit and Apple Watch.

But it’s not just about the quantity of data that can be harvested; it’s about how the data is used. Data can be structured in a system that enables the insurer to make a decision based on actionable insights. This can also alleviate some of the concerns about privacy. Collecting the street names that a customer has driven down may be seen as an invasion of privacy, but categorising (or grading) the roads or areas in the UK based on historical accidents that have occurred there allows customers to maintain location privacy, while still allowing insurers to determine the driver’s risk. It’s also far more helpful to the insurer to know that a customer drives on low-risk roads 78% of the time and medium-risk roads 15% of the time than an unstructured stream of road names.

This is just a single application of Big Data in the insurance industry, currently being used in the real world. But the more information that insurance companies can be provided with, the more accurate the risk analyses can be and the fairer pricing can be for everyone.

Data can be generated through a number of sources, including smart home devices such as thermostats, home security systems and kitchen appliances. For example: in 2014 US companies, Liberty Mutual Insurance and American Family Insurance, partnered with Google-owned Nest Protect, offering their smart smoke alarm and carbon monoxide monitor to customers for free. The deal meant that customers receive the device for free in addition to discounted premiums from their insurers, and insurers had access to the wealth of data being received from the device. Everybody wins.

Social media websites such as Facebook, Twitter and Instagram also allow insurance companies to freely scrape the data of individuals or certain demographic groups to gain better understanding of behaviour and risks. This can be done without the knowledge or approval of the user, as they have already consented to the sharing of their information when making their social media profile public.

The possibilities are indeed endless, and insurance companies will have to be forward-thinking when approaching new strategies to make the industry better for everyone.

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