top of page
  • Shivani Pai

7 Use Cases of Big Data in the Insurance Industry

7 Use Cases of Big Data in the Insurance Industry

With advancements in technology, the dependency and relevance of data have increased. As a result, several industries are leveraging data insights to improve their operations. Be it the healthcare industry, sports, or even the insurance industry.

The Insurance Industry is one of the most innovative and rapidly-changing industries in the United States. The use of Big Data has been a critical part of this innovation, as it allows insurers to collect, analyze, and interpret information more quickly and effectively than ever before.

There is a significant rise in the application of big data tools, and the companies that have invested in big data analytics witnessed 30% more efficiency, 40% to 70% cost savings, and a 60% increase in fraud detection rates. In addition, with big data, insurance companies can target their customers more precisely.

Role of Big Data in insurance

Data analytics has always been integral to the insurance industry to target customers. For example, accident statistics, policyholders' personal information, and third-party sources give an understanding of who falls under which risk category. Such data can also prevent fraud losses and optimize expenses.

Now, with widespread digitization, there's more data available to understand a customer's behavioral patterns and determine the segment they could belong to. In addition, insurance agencies use unstructured and structured data better to handle pricing, marketing, and claims handling.

According to research, 300 fraudulent claims and over 2,000 dishonest applications get detected daily. With big data, you can search for anomalies, analyze social network information, and fight fraud.

"Insurance companies have a ton of data. Our goal is to use this data to create and design new innovative insurance products with our insurance carrier partners," - said Paul Ford, CEO, and co-founder of Traffk.

Here are a few ways how data assists in ascertaining risks:

  • Ease of accessibility and the availability of a customer's medical information. By wearing fitness trackers that monitor your heart rate or even calories, one can know how an individual's medical condition might impact future health and longevity.

  • Information on whether the consumer is financially stable or their spending pattern will let you predict their behavior and lifestyle.

  • Continuous and immediate motor vehicle information, including location, driver behavior, and engine information.

  • Natural disaster event information allows a better understanding of locations and levels of future events, e.g., flood hazard mapping.

  • Information from internet-connected devices in the home or business, such as smoke detectors or water usage monitors.

7 Use Cases of Big Data in the Insurance Industry

1. Customer Acquisition

Businesses need customers to generate revenue; when you have data about what motivates your target group, it is easier to acquire them. In this social media era, there's massive data generation. This unstructured data is a significant part of big data that one can use for analysis by the insurance companies to build targeted campaigns. It is also relatively more precise than surveys and questionnaires.

2. Customer Retention

Customer retention is essential, and businesses that can do that successfully will be able to sustain themselves in the market. For example, a study states that 32% of business leaders prioritize customer retention, while 80% of profits come from only 20% of current clients. Using big data, you can determine what made a customer quit your service/company. When you understand what causes customer dissatisfaction, work on it by improving your services and even solving their grievances.

3. Risk Assessment

Insurance companies work on the principle of risk. They always deal with risk and subsequently verify customers' information while assessing risks. Customers get put into different groups based on their risk factors. Big data helps identify and forecast risks. For instance, an athlete will not have much health risk compared to an obese person. Data from their dietary habits and lifestyle can help companies ascertain who falls under which risk class.

4. Fraud Prevention and Detection:

The total cost of insurance fraud, in non-health insurance, is more than $40 billion annually in the United States. It costs the average U.S. family between $400 and $700 per year through increased premiums. Big data will help in saving insurance companies against such frauds. You can use predictive modeling to compare a person's data against past fraudulent profiles and identify cases that require more investigation.

5. Cost Reductions

Technological advancements have been a boon with their cost-cutting measures. The increased role of machines in your daily operations will increase efficiency and bring down costs. Employ big data technology to automate manual processes. As a result, the time and effort spent on handling claims and administration get significantly reduced. It will also impact premium amounts, where you get to charge lower premiums and enable customer retention.

6. Personalized Service and Pricing

Every customer likes special treatment. There is a need for a personalized experience, and companies also know about this need. BCG's report states that insurers must have dynamic pricing to maintain a competitive advantage. When you have data about the customer's needs, you can create a plan that meets their requirements. Then, utilize the data to decide on a pricing model that fits the client's budget and is profitable for the company.

7. Effects on internal processes

The implementation of big data tools will increase efficiency by automating a lot of processes. For example, this technology will allow you to work on customer profiles efficiently. Be it checking their history, segmenting them into different risk classes, or automating claims processing. Automated claims have reduced manual work by 80% and improved accuracy significantly.

Unleash the power of Big Data in your insurance agency

Big data is a powerful tool for the insurance industry. With it, insurers can better assess risk and run more accurate simulations of potential outcomes. This means lower premiums for customers and higher revenues for the company.

By now, most insurance companies have tapped into the big data world, while some are still trying to understand the basis of its appeal. However, the first step is to obtain high-quality web data.

A lot of insurance companies are leveraging big data insights to conduct better business. But how can you obtain high-quality data at scale? Fortunately, Datahut is here to help.

Datahut is a web scraping service provider that helps businesses get structured data feeds from any website through our cloud-based data as a service platform. We provide web scraping services for all types of businesses, including those who need to collect data from various sources (like social media) and turn it into useful information.

We offer flexible plans that allow you to choose what works best for your company—whether it's one-time or recurring scraping services, or something more customized. Our team is dedicated to providing high-quality customer support and fast turnaround times, so we'll be ready when you need us!

Eager to explore how Datahut can help you? Contact us today



Do you want to offload the dull, complex, and labour-intensive web scraping task to an expert?

bottom of page