Technology has long since become an integral part and parcel of how organizations function and conduct their business. As companies grow and increase the number of their policyholders, the data residing in their databases also increases exponentially in both volume and variety. The need to make meaningful analysis of this data also arises. This calls for a big data analytics solution. Insurers rely upon big data analytics heavily to make sense of huge volumes of data residing in their databases. This blog explores ways big data analytics has impacted the insurance industry.

Big Data and the Insurance Industry

Insurance companies have always run on data – no question about it. They have relied and run upon the visible, in-house, and structured data such as customer data on the policy form, historical behavior of policy renewal or termination, etc. But insurers can rely upon many more sources of data such as social media profiles and their behavior, claims notes and internal notes, publicly available government data such as criminal records, bankruptcies, foreclosures, etc. These forms of unstructured data call upon new storage technologies, and computing systems to handle such vast amounts of data. This is called big data and in order to make sense of the same, one must employ big data analytics. In order to succeed and compete in such a competitive industry, insurance firms have to employ big data analytics to reap the benefits it offers.


Impact of Big Data Analytics on the Insurance Industry

Fraud Prevention and Detection

One of the biggest issues insurance companies are facing right now is fraud. According to industry findings, every 1 out of ten claims filed is a fraudulent one. Such fraudulent claims waste resources meant for genuine claims and drive up the premium for other policyholders. While detecting fraudulent claims, a lot of processing has to be done within a short amount of time and on a huge volume of data. Big data analytics can deliver insights in a very short amount of time or near real-time. Such analytics techniques can sift through a huge amount of data and compare whether an applicant has past fraud convictions. Also, predictive modeling analytics techniques can study trends in the applicant’s claim record and third-party data to determine if he is predisposed to make a fraudulent claim. Methodologies in data analytics such as database searches, modeling, text mining, and exception reporting are used in identifying fraudulent claims. For example, a large property and casualty firm increased the fraud detection success rate from 50 percent to 88 percent and also reduced the time required to refer questionable claims for investigation by nearly 95 percent by leveraging big data analytics.

Proper Fund Balancing

Insurers maintain huge amounts of funds against potential claims. It is nearly impossible to predict when a potential claim might arise. But with the rise of IoT and social media data, big data analytics techniques can predict with a high accuracy when this might happen. Big data analytics techniques can help optimize the reserve funds against potential claims.

Customer Segmentation

Insurers can group customers based on parameters such as age, gender, location etc. Within a particular group, applying the same premium makes sense as this compensates the high-risk customers with premiums from low-risk customers. Big data analytics techniques can help insurers group customers better, optimize risk, and understand trends and patterns among customers to make better decisions.

Pricing and Underwriting

Estimating the price of an insurance policy based on various complex risk assessment procedures is the bread and butter of the insurance companies. In its latest report, The European Insurance and Occupational Pensions Authority found out that this area was benefitted in a major way with the use of big data analytics techniques. The sectors seeing the most benefit with the use of smart IoT devices generating data in real-time are motor, health and life insurance. Big data analytics can help tailor customized premiums based on individual behavior and also insights generated from big data analytics can help create customized policies for individual users.


The adoption of big data analytics solutions by the insurance industry is rising. Insurance companies have invested $3.6 billion in 2021. Companies investing in big data analytics have seen 30 percent more efficiency, 40 to 70 percent cost savings and 60 percent increase in fraud detection rates that benefit both the customers and companies themselves. Big data analytics techniques have changed the insurance industry for good. Its implications have allowed insurance firms to target their customers more precisely.