Case Studies in the Insurance Industry Using Big Data

Insurance Industry Using Big Data – Case studies in the insurance industry using big data, large data sets with high accuracy, speed, and variety have become vital tools for insurance businesses because the sector was formed to foresee future events and assess the risk/value of these events.

The opportunity to use big data in these new areas of the industry is even more interesting today because of the new data sources such as telematics, sensors, government, customer contact, and social media.

Insurance businesses are able to better estimate risk, claims, and customer satisfaction thanks to the usage of Big Data technology. In the insurance industry, big data and related technology are used in a variety of ways.

Risk assessment

For insurance businesses, determining rates is a critical function. Telematics (in-vehicle telecommunication devices), IoT devices, and wearables (Fitbit, Apple Watch, etc.) are used by numerous insurance firms to track their customers and anticipate and quantify risk.

By combining behavioural data with exogenous factors, such as road conditions or a safe atmosphere, insurance firms can anticipate if a driver would be involved in an accident or have their car stolen.

Wearable technology is having an impact on the health and life insurance industry as well. Activity trackers can keep tabs on a user’s behaviour and habits and provide a real-time assessment of their level of physical activity.

Detection of Fraud

Data management and predictive modelling are used by insurance companies to detect fraud and criminal activities. Accordingly, they compare claim data with fraudulent claims characteristics, and when a match occurs they pin down the claim for further examination.

The claimant’s behaviour, the network of persons connected to them (social media, credit reference agencies, etc.), and the partners participating in the claim may also be taken into consideration (eg vehicle repair shop). Intricate matches may have gone unnoticed by humans, but big data analysis picked them out.

Insights from Customers

This information is crucial for insurance firms because it allows them to anticipate customers’ future actions by offering relevant products as well as determining the appropriate segmentation of their customers’ needs.

Insurance businesses are able to develop unique client profiles using information gleaned from call centre data, customer email, social media, user forums, and user behaviour while registering into insurance company websites. When a high volume of calls to the helpline is detected, the analytics system knows that a customer is on the verge of leaving.

Big data analytics may assist insurance businesses build trusting relationships with their customers and engage them in the correct way with accurate information in addition to providing predictions about when a client is most likely to leave or shaping consumer policy. Insurance firms benefit from strategic learning in a variety of ways, including the ability to solve client problems in real time and cross-sell and upsell other products.

Advertising and promotion

A thorough understanding of client behaviour helps insurance businesses better focus their products and services to specific customers. Personalized services and goods, such as reducing premiums (primarily utilised by auto, home, and health insurance firms), contacting consumers for special offers when they are most likely to depart or even giving family packages when a family might have a baby, are some of the ways this is done.

Loyalty Programs for Customer Experience

Isn’t that news? For the first time ever, insurance companies are tailoring their products and services to meet the needs of their consumers based on their preferences and behavioural data.

Health insurers, in particular, are making use of wearable app and device data to keep track of their customers and assist them in managing their chronic health issues. With the help of Scipt Hub Plus, patients can find out their exact medication costs, based on their insurance coverage, at the pharmacy of their choice, right when they go to the doctor to pick up their prescription. In order to prevent and control diabetes, Cigna has teamed up with BodyMedia to employ the brand’s armband tracker, which is integrated with the customer’s insurance plan.

In the life insurance industry, Haven Life (an online term life insurance provider) uses big data technology to help users quickly make decisions regarding policies of up to $1 million through online surveys, prescription history, state motor vehicle records and other data sources.

P&C insurance companies also help their customers improve their safety by working with them. State Agricultural Insurance Company’s Driver Feedback App analyses clients’ driving patterns and provides advice on how to improve them. It’s a definite win-win situation.


Simple operations like compliance checks, data entry, or other repetitive tasks that don’t take a lot of initiative can be automated with insurers. To name just a few of the more complex skills enabled by the advent of big data technology, such as fraud detection and loan underwriting are now within reach of anyone with a basic understanding of the technology.

Using machine learning to train algorithms and, of course, predictive analytics, insurance businesses may save a significant amount time and money by moving toward smarter automation.

Improved Employee and Financial Intelligence

It is now possible to adjust premium rates and coverage limits every day using real-time analytics, which combines internal data (policies, regulations) with data from external sources like social media, press and analyst comments in order to optimise finance and instant payments. they.

Classifying and evaluating claims using data mining techniques is another way of ensuring that the most qualified workers receive the most difficult claims. This saves insurers a lot of time and keeps them from having to pay out a lot of money in claims.

Although there are some drawbacks to big data, it is undeniably a powerful tool that improves the insurance industry’s ability to make better strategic decisions.

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