Big Data in Insurance: Use Cases and Future Prospects

Big data has swiftly become one of the insurance industry's most powerful buzzwords. It's a priceless tool for analyzing trends and patterns, which insurance brokers can use to inform policy, assess risk, and detect fraud. Big data has swiftly become one of the insurance industry's most powerful buzzwords. It's a priceless tool for analyzing trends and patterns, which insurance brokers can use to inform policy, assess risk, and detect fraud.

 

Most insurance carriers are currently embracing big data to some extent, but the uses of big data are continually expanding and changing year after year. Understanding big data and how it can be used to provide efficient, smart insurance products to discerning clients should be the first step in any insurer's game plan.

 

What is Big Data? 

 

Big data refers to vast, diversified sets of information that are growing at an exponential rate. It includes the volume of information, the velocity or speed with which it is created and collected, and the variety or scope of the data points covered (the "three v's" of big data). Big data is frequently derived by data mining and arrives in a variety of formats.

 

Big data refers to data that is massive in terms of volume (size), variety, and velocity (speed).  Consider this: You view a YouTube video, like it, and share it with a few pals. You then go online to buy groceries and medicine, as well as look for fun places to visit. You fire up Netflix and start watching your favorite web series. You pay your parents' phone and electric bills, as well as update their information on a health portal in order to apply for insurance. When a friend phones to ask you to like their Instagram content, you log in and remark on a few of their photographs.

 

Then you arrange a flight to your folks' house for the following weekend. With each purchase, you generate data and share personal information about yourself and those to whom you are related—your parents, friends, favorite series, preferred trip destinations, and so on.

 

As you continue to transact in numerous ways, the volume and variety of data expands at an alarming rate. And that's just your information! Consider how much data each of the world's 4.66 billion active internet users generates on a daily basis! Data can be generated in a variety of ways, including the fitness app you use, doctor visits you arrange, or movies you watch, Instagram posts you like, food shopping you make online, games you play, holidays you book, and every transaction you make (or cancel). Businesses frequently examine that data in order to better understand their users and present them with personalized content.

 

Almost all major businesses use big data to optimize operations and cut overall expenses. Big data in healthcare, for example, is becoming increasingly important—early disease identification, medication discovery, and personalized treatment plans for patients are all examples of big data uses in healthcare.

 

Despite the ethical concerns that come with Big Data, there are numerous advantages to using it properly. Big Data, for example, can be utilized to enhance healthcare outcomes by detecting trends and patterns that can be used to inform treatment decisions. It can also be used to increase corporate efficiency and effectiveness by identifying problem areas.

 

Also Read | Ethics of Big Data: Balancing the Risks and Rewards

 

Big data in Insurance 

 

Most insurance businesses recognize that big data should be at the heart of most of their operations, but just a handful actually understand how to analyze it and apply it to their business. In reality, big data can be used to improve practically every part of the insurance industry, from underwriting to claims management and customer service.

 

According to research published by the European Insurance and Occupational Pensions Authority (EIOPA), the most important use of big data in insurance today is in pricing and underwriting. A good example is auto insurance, where brokers may compare individual driving behavior to a large data set to properly anticipate risk and customize plans to each motorist.

 

In the claims management process, insurers can utilize big data to analyze loss or damage and segment or, in certain situations, automate claims. This makes it considerably easier for providers to make important claims decisions, such as whether or not to pay a claim.

 

One of the most intriguing applications of big data is when it is used to forecast and even modify customer behavior. This is related to the Internet of Things; insurers who can accurately analyze client behavior using data from a wide range of devices may be able to intervene before a claim is made to remind policyholders to change high-risk behaviors, such as driving too fast or failing to activate a burglar alarm.

 

Big data is a valuable resource for the insurance industry. Insurance companies can use it to better analyze risk and execute more realistic simulations of potential scenarios. This equals cheaper client premiums and increased revenue for the corporation.

 

Most insurance businesses have tapped into the world of big data by now, while others are still attempting to figure out what makes it so appealing. The first step, though, is to collect high-quality web data. 

 

Use Cases of Big Data in Insurance

 

To target clients, data analytics has always been essential in the insurance industry. Accident statistics, policyholder personal information, and third-party sources, for example, provide insight into who fits into which risk group. Such data can also be used to reduce fraud losses and optimize expenses. 

 

With growing digitalization, more data is now available to analyze a customer's behavioral habits and determine the category to which they may belong. Furthermore, insurance companies make better use of unstructured and structured data in pricing, marketing, and claims management. Big data is being used in the insurance industry in a variety of ways. The seven of them are listed below:

 

1. Customer Obtainment:

 

Businesses require consumers to produce income; having data on what motivates your target demographic makes it easier to recruit them. Massive amounts of data are being generated in this social media era. This unstructured data is a crucial component of big data that insurance companies can use to create targeted advertising. It is also more accurate than surveys and questionnaires.

 

2. Pricing Optimization:

 

How do you price a thing when you don't know what it costs to make? This is a problem that the insurance business faces on a daily basis.  The cost of labor and raw materials are known in most businesses, and a profit margin is calculated to determine the price of the product. Insurers, on the other hand, have no idea how much a product costs when it is sold.  The real price of the goods may not be known until all claims have been paid.

 

3. Risk Evaluation:

 

The entire concept of insurance firms centers around risk diversification. While assessing risks, insurers have always prioritized the verification of consumer information. Based on their data, customers are classified into several risk classes. 

 

4. Cost-cutting measures:

 

With their cost-cutting strategies, technological advances have been a blessing. Machines playing a larger role in your daily operations will boost efficiency and save expenses. Using big data technology, you can automate manual operations. As a result, the amount of time and effort spent on claims and administration is greatly decreased. It will also have an effect on premium amounts, allowing you to charge reduced rates and retain customers. 

 

5. Pricing and Service Customization:

 

Every consumer appreciates personalized service. There is a demand for a tailored experience, and businesses are aware of it. According to the BCG analysis, insurers must have dynamic pricing in order to preserve a competitive advantage. You may build a plan that meets the needs of the customer once you have data about their wants. The data should then be used to determine a pricing model that suits the client's budget while also being lucrative for the organization. 

 

6. Internal Processes Affected: 

 

The use of big data algorithms can assist improve the efficiency of most operations that need extensive brainstorming. Insurers can work swiftly on a customer's profile thanks to big data technology. They can review their history, select an appropriate risk class, develop a pricing model, automate claims processing, and provide the finest services. According to a McKinsey and Company study, automation saves insurance staff 43% of their time. 

 

Also Read | 7 Uses of Big data in the insurance industry

 

Future of Big Data in Insurance

 

The majority of the data is dispersed within and outside the firm and is unstructured, making it difficult for an insurer to get insights. They must have strong data management capabilities in order to cleanse and combine external and internal data and acquire a 360-degree view of the policyholder and his/her behaviors.

 

All of this necessitates significant investments in technology, the acquisition of new personnel, and possibly even the transformation of the corporate culture to better develop the type of attitude and thinking required to prioritize whatever type of data to collect and how to effectively use it.

 

Big data, unlike some other trendy subjects, is not a transitory fad. As more IoT devices come online and customer behaviors change, the potential provided by big data in insurance will expand, as will the cloud's capacity to store such massive amounts of data. According to IDC predictions, the global datasphere will exceed 175 zettabytes by 2025. With the worldwide capacity to collect and store data increasing, as well as improvements in AI and machine learning technology, insurers must carefully examine their technology stacks to guarantee they can remain competitive and respond to increasing client demand.