Insurance companies are considered to be among the most likely potential adversaries to have the motivation to attempt a re-identification, and are considered to have the necessary tools.
Financial gain. Insurance companies collect data from many sources. They merge them in many ways to calculate risk (expected lifespan, weather patterns for farming insurance, car accidents etc.). They then calculate premiums based on that risk. The better/more data they have the better they can price the risk and the more they can manage their profits.
One of the most important uses of data analysis for insurers is determining policy premiums. For example, automobile, home and health insurance companies use data from telematics (in-vehicle telecommunication devices) IoT devices and wearables (Fitbit, Apple Watch etc.) to track their customers in order to predict and calculate risks.
Insurers also use data to improve fraud detection and criminal activity through data management and predictive modelling by matching the variables in every claim against the profiles of past claims which were fraudulent so that when there is a match, the claim is pinned for further investigation.
Information gained from call centre data, customer e-mails, social media, user forums and user behaviour while logged into the insurers’ sites, enable insurers to build unique customer profiles with customer behaviours, habits and needs to anticipate future behaviours for up-selling/cross-selling products.
Since the insurance industry is founded on estimating future events and measuring the risk/value of these events, the volume, velocity, veracity and variety of massive datasets has become an essential tool for insurers. With new data sources such as telematics, sensors, government, customer interactions and social media, the opportunity to use big data to determine risk, claims and enhance customer experience (higher predictive accuracy) is very appealing to this industry.