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7 Uses of Big data in the insurance industry

  • Ritesh Pathak
  • Jan 09, 2021
  • Updated on: May 25, 2021
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The advancements in technology have led to the exchange of massive amounts of data that contain large volumes of structured and unstructured data, referred to as big data. Every industry is leveraging this big data for its own benefits. Big companies like Amazon and Starbucks use big data for the purpose of supply chain management, personalized recommendation systems, and many more. So, the insurance industry is no exception. 


Insurance companies work to protect us and help us in certain situations. They form a pool of money, taken from different clients which we also call policyholders. These companies guarantee us to give a sum of money when we require. The insurance industry, like any other industry, has also shifted towards digital platforms. So, the exchange of data over the internet allows the insurance companies to utilize the technology of big data. 


This blog will focus on -

  • Functioning of insurance companies.
  • Role of big data technology in the insurance industry.
  • Ways in which big data is being leveraged by insurance companies to target their customers. 


Also Read - Application of Big data in the sports industry



How do insurance companies work?


The insurance sector companies work to guarantee insurance contracts for uncertain situations. The industry comprises different players that operate in different spaces. The insurance industry works on a basic concept where one party, that we call an insurer, guarantees payment for uncertain future events. Meanwhile, the policyholder, also called the insured, pays a premium at fixed intervals to the insurer in exchange for that protection on that uncertain future occurrence. 


As an industry, the insurance industry is considered a slow-growing industry and a safe sector for investors. We often hear the term underwriting in the insurance industry which refers to the process of taking financial risk for a fee by an individual or an institution. 


Insurance companies form their plan of action/business model on the basic idea of anticipating and diversifying risk. The fundamental insurance model involves consolidating risk from individuals payer and reallocating it across a larger portfolio. 


Recommended blog - Types of Insurance


There are two ways in which insurance companies generate revenue. The first is by charging premiums from individuals for insurance coverage and the second is reinvesting those premiums into other interest-generating assets. As we have mentioned above, there are different types of insurance companies that serve different customer bases. 



How insurance companies use technology?


The insurance industry, for a long time, has been known for leveraging traditional business models. The industry continued its legacy business and products for quite some time. But with the intervention of modern-day technologies, the industry witnessed some favorable outcomes. The industry has witnessed the exponential growth of the use of technology like any other sector. Advanced technologies and digital platforms have allowed insurance companies to try new means of tracking, measuring, and controlling risk. 


The introduction of various technologies has evolved the insurance landscape. Tech solutions have enriched the industry. Some of the key technologies that are being used are the Internet of Things, artificial intelligence, Blockchain, Machine Learning, Big data analytics, and Insurance Management platforms. 


Let us dive deep into this use of technology, especially big data, by insurance companies to better target their customers. 



Big Data in the insurance industry


The insurance industry has always thrived on data analytics to target its customers. Different types of insurance companies such as travel insurance companies, health, and life insurance companies, P&C insurance companies, etc., rely on statistics to segment their customers. Accident statistics, policyholder’s personal information, as well as third-party sources, help to group people into different risk categories, prevent fraud losses, and optimize expenses. 


The shift towards digital platforms has opened the door for new sources of information that can be used to understand the complex behavioral patterns of a customer and precisely determine his or her segment. For insurance purposes, big data refers to unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing, and claims handling.


“Going forward, access to data, and the ability to derive new risk-related insights from it will be a key factor for competitiveness in the insurance industry. New approaches to encourage prudent behavior can be envisaged through Big Data, thus new technologies allow the role of insurance to evolve from pure risk protection towards risk prediction and prevention.”

- Anna Maria D’Hulster, Secretary-General, The Geneva Association


You can also sneak a peek at our blog on 5 ways in which Big Data Analytics is building businesses



7 Ways in which big data is used in the insurance industry


The insurance industry is using big data in several ways. We have listed the seven of them below. 


The image mentions the 7 uses of big data by insurance companies i.e. customer acquisition, customer retention, risk assessment, fraud protection, personalized services, cost reductions, and effects on internal processes.

7 ways in which big data is helping the insurance industry


1. Customer Acquisition


Every business needs to acquire customers to generate revenue and if the process of acquisition can be made efficient, that would make things simpler. In the days of social media and the increased use of the internet, every person generates massive amounts of data via social networks, emails, and feedback. 


The data collected from the online behavior of customers is categorized as unstructured data and a part of big data. Analyzing such unstructured data, insurance companies can create targeted marketing campaigns that will acquire new customers. Tracking this online behavior of customers gives much more precise information than any survey and questionnaire. 



2. Customer Retention


No business likes to lose its customer base. A business is considered to be successful if its customer retention rate is higher. The insurance industry is no exception. So, it utilizes big data to retain its customers, who may part their ways with the company. 


Based on customer activity, algorithms can predict the early signs of customer dissatisfaction. Working on the insights provided, companies can quickly react to improve their services and also find a solution to the grievances of that particular customer. Insurers can offer discounts or even change the pricing model for the client. 



3. Risk Assessment


The whole idea of insurance companies revolves around diversifying risk. Insurers have always focused on the verification of customers’ information while assessing the risks. Customers are segmented into different risk classes based on their data. 


Big data technology can increase the efficiency of the whole process of risk assessment. Before arriving at a final decision, an insurance company can utilize big data and use predictive modeling to count on possible issues, based on client’s data, and furthermore put them into a suitable risk class. 


4. Fraud Prevention and Detection


According to Coalition Against Insurance Fraud, each year the United States’ insurance companies lose more than $80 billion due to fraud. Such fraudulent acts result in increased premiums for every stakeholder. 


Big data can be used to save insurance companies against such frauds. Using predictive modeling, insurers can compare a person's data against past fraudulent profiles and identify cases that require more investigation. 



5. Cost Reductions


Cost-cutting is one of the many benefits of leveraging technology. The increased role of machines in the industry increases efficiency which eventually leads to cost reductions. 


Big data technology can be leveraged to automate manual processes, making them more efficient and reducing the costs spent on handling claims and administration. This will allow the companies to offer lower premiums to their clients and hence stand tall in the competitive market. 



6. Personalized Service and Pricing


We all like to be treated specially. Companies have understood the need for a personalized experience. The analysis of unstructured data can help companies to offer services that suit and meet the customers’ needs. 


For example, life insurance based on big data can become more personalized by taking into account the medical history of a customer along with the habits perceived by the activity trackers. The data can also be utilized to decide a pricing model that fits into the budget of the client and also is profitable for the company. 



7. Effects on internal processes 


The implementation of big data algorithms can help increase the efficiency of most of the processes that require deep brainstorming. Big data technology allows insurers to work quickly on a customer’s profile. They can check their history, decide on a suitable risk class, form a pricing model, automate claims processing, and deliver the best services. A study by McKinsey and Company shows that automation saves 43% of the time of insurance employees. 




How is big data affecting different segments?


When we look at the impact of big data technology on the insurance industry, it is quite evident that it has worked wonders for the insurance companies. The application of big data has already started benefitting insurance companies. We should also learn about the impact of big data on each particular sphere of the insurance sector. 



1. Big Data in Health and Life Insurance


Including new information sources, insurance companies can for insurance models that will be more targeted and will also encourage customers to improve their lifestyle by offering discounts on increased activity. John Hancock, one of the oldest and largest North American life insurers, announced last year that he will be only selling interactive policies based on the data generated by health apps and wearable devices. 


In any case, the ramifications of Big Data in medical coverage causes concerns identified with data security, protection, and morals. This field actually expects enactment to guarantee that punishing unfortunate conduct doesn't hurt the individuals who truly need insurance.



2. Big Data in P&C Insurance


The situation is all the more encouraging for property and casualty insurance, as Big Data can assist with recognizing exact connections between client conduct and dangers. For instance, vehicle insurance agencies can grade roads dependent on the accidents that occurred and check their customers' tracks. With Big Data, vehicle protection can get an exceptionally customized client profile dependent on drivers' GPS locational information and use it to settle on an ultimate conclusion. As GPS information is protected, such a cycle doesn't breach customers' privacy.



3. Big Data in Travel Insurance


Contrasted with different fragments, travel protection embraces big data and, especially AI advancements, very well. (You can also spare a glance at our blog on Big Data in Tourism) The relatively low price value settles on travel insurance, a genuinely brisk choice, so this industry manages an amazing number of solicitations. Innovations can speed up the interaction with customers, give more tailored products and services, automate simple communication, improve customer satisfaction, and quickly configure the most beneficial offer. 





The adoption of Big Data in the insurance industry is constantly increasing. SNS Telecom & IT expects the insurance companies to invest up to $3.6 billion by 2021. According to Yes Magazine, the implementation of big data results in 30% better access to insurance services, 40-70% cost savings, and 60% higher fraud detection rates that benefit both customers and stakeholders. 


We can say that big data has revolutionized the insurance industry for good. Its implications have allowed insurers to target customers more precisely. 

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