Data science in Insurance

Insurance is all about risk, but risk is a complicated concept. The insurance industry has always run on statistics to make sense of those risks. Actuaries (possibly the original data scientists), who calculate the likelihood of certain catastrophic events that might require policy payouts, have been a mainstay at insurance companies since the 17th century. Today, actuaries are as important as ever, but the data from which they make their calculations has vastly expanded. Data scientists at insurance companies are now pulling in information like: Government-tracked health, climate, and epidemiological data, vehicle instrumentation and tracking data, satellite terrain and geographic information, credit reports and economic data, telematics devices, smart phones, cctv footage, electoral rolls, credit reports, website analytics.

These sources tell insurers far more than did historical data from policy administration systems, claims management applications and billing systems, and the mortality reports of yesteryear. Through a judicious analysis of big data, insurers have now been empowered to improve their pricing accuracy, create customized products and services, forge stronger customer relationships and facilitate more effective loss prevention. That’s good news for budding insurance data scientists. As big data continues its exponential growth, insurers are going to need help in deciding how to put it to good use. Once upon a time, insurance agents were like local doctors – they knew individuals and communities inside-out. That meant they were aware of the risks in selling a policy to the town drunk. To match that level of knowledge in the age of decentralization and the Internet, the insurance industry is turning to big data. Now, insurance data scientists are combining analytical applications – e.g., behavioral models based on customer profile data – with a continuous stream of real-time data – e.g., satellite data, weather reports, vehicle sensors – to create detailed and personalized assessments of risk.