5 Ways To Use Insurance Data (And How To Collect It)

Insurance data has increased exponentially, and this is because more people are beginning to understand the importance of being insured. I wish I had listened to my sister when she told me about insurance years ago, but it’s never too late! I did my research, and I can say for a fact that I made the right decision when I became insured last year. In a pandemic where businesses were on hold and funds were short, many insurance companies came through for their customers. U.S. auto insurers dispersed more than $14 billion to their clients across the nation. 

However, as the industry continues to expand, so does the amount of data revolving around it. The insurance industry is now one of the top sectors where big data analytics has a vital role. There are millions of useful insurance data for insurers or consumers on the internet. Knowing how to leverage big data will give you a significant edge in the industry as an insurance provider. In this article, we will discuss some of the best ways you can leverage insurance big data. If you are familiar with what insurance data is all about, then you can skip to the juicy part with the table of contents below.

Table of Contents

Meaning of Insurance Data 

What exactly are we referring to when we say insurance data? Insurance data falls into two categories: data for consumers and data for companies.

Data for consumers

If you, like me, have had your eyes opened to the benefits of being insured and now you are looking to get started, you need data. As a consumer, you need to identify the providers offering what you need. Then you need to research each of these companies to know their history with claimants, their reliability, their financial stability, etc. In short, you need all the data you can find on them.

Data for companies

If, on the other hand, you are an insurance provider, then you, just like any other business, need data on your consumers and the market. Several insurance businesses have adapted the method of surveying alternative data to plan their policies, services, and products. This data helps you to identify the right people to pitch your services to. A person who has a family member that has benefited from being insured is more likely to want insurance than someone whose friend was ripped off in an insurance scam. With the way people report everything about them on social media nowadays, you can easily access this information. 

Insurance data is useful to both the insurer and the insured. By utilizing big data in insurance, you can access a wealth of information and make informed decisions in real-time. You can use this data to estimate the reward or risks involved before making a policy. It has a plethora of other benefits, which makes it crucial in insurance. 

Unfortunately, as remains the major issue with big data operations, manually processing big data for valuable insurance insights is the closest thing to impossible on this side of the universe. The data you need as an insurance provider is not limited to the industry. You have to know everything about your prospects, not just in the context of insurance, but about their whole life. The death of a relative might be all it takes to make a prospect decide to get insured. 

But how do you handle these massive amounts of data? How do you collect and collate them into a single location to reference when you need it? Despite the rise in the use of data libraries, most of the data you need does not exist in a form where you can just push a button and download it. To efficiently implement a strategy for the use of big data in insurance, you need a specialized data collection solution. Here’s where web scraping comes in.

How To Use Web Scraping To Gather Insurance Big Data

You have probably come across the term web scraping before, in your journey into the world of big data. And you’ve wondered what it is and how it works. You can stop wondering now. 

I’m quite sure you have carried out a Google search more than once before. And when you get to the results page, you click different links to get information from them before making a decision. Well, what you have done is web scraping. 

At the micro-level, web scraping is simply the act of collecting data from the internet in any form. However, web scraping at the macro-level allows you to collect data in large volumes by using bots. The bots, called “crawlers” or “spiders,” parse through the source code of a given web page and tag data according to some preset parameters. Then a data extractor collects the tagged data and extracts it into a spreadsheet file. This essentially is how web scraping works.

Web scraping in insurance means you can extract bulk data accessible on the internet to aid your company’s proposal forms, underwriting decisions, and claims appraisal. You can use it to implement real-time data analytics and track changes in lifestyle or newsworthy events that could lead to a boom in insurance needs. You can also make smart moves with the information you crawl data from insurance companies’ websites as a consumer. 

Application of Big Data in the Insurance Industry

Insurance companies require a cloud-based data-driven broker management platform to function best. With insurance data, you can examine, accumulate, and devise strategies for your business. I haven’t even delved into all the many valuable reports that will be accessible to you any time to help you understand the trends better and their applicability.  

There are so many ways you can benefit from implementing insurance data into your operations. For now, let us take a look at four vital applications of big data in the insurance industry.

1. Competitor monitoring: Insurance companies need to extract big data to access the variety of coverage plans within the field. If you work for such a company, scraping could help you know how to develop better strategies than your competitors. That way, you will have the edge over them and win clients over to your company. 
2. Underwriting decisions: Underwriting is an important aspect of insurance. It is one of the foundations upon which insurance was built, and the majority of underwriting models are acquired through risk studies, customer feedback, historical data, and proposal forms. By scraping insurance data, you can predict relatively accurate underwriting, which allows you to create reasonable terms for you and your clients. Furthermore, it helps to determine better price policies and specific risks. 
3. Accumulating metrics: For insurance companies, it is imperative to make metrics available to your audience on your website. Details like the percentage of claim rejections, the percentage of clients who got insurance claims, and more are required on your website by even the government. Having the metrics of the clients who successfully got their claims builds trust and loyalty in potential or new ones. Many insurance companies don’t come through when it’s time to deliver their end of the bargain, so the metrics on your website proves you’re trustworthy. As a consumer, you can scrape different insurance companies’ websites to get the best deals. 
4. Health insurance analytics: Health insurance data is as essential as any data sets within the insurance sector. There are hardly any hospitals that aren’t in conjunction with insurance companies. As a consumer, choosing the right health insurance company for you is a step you should handle meticulously. You can use a web scraping method to extract data from as many insurance providers as you want. Different companies cover different diseases in their contracts. If necessary, you can gather health insurance analytics on multiple companies so you will associate with the ones that match your requirements. 
5. Financial analysis: Suppose you specialize in insuring companies and businesses, big or small. In that case, you need to carry out a financial analysis to determine a company’s profitability and its risk ratio before taking them on as a client. By collecting big datasets from platforms like Yahoo! Finance, you can track your clients’ finances and that of potential clients. Keeping an eye on the finances of your clients is essential when insuring big companies. A company that is prone to losing money can be a liability to you as an insurer.

How To Apply Big Data in Insurance With Scraping Robot

Like I said earlier, to efficiently use insurance data, you need a specialized data collection method. Web scraping is that method. However, to successfully implement the method, you need the right tool. Scraping Robot is the right tool. At Scraping Robot, we have developed a cutting edge scraping technology that allows you to collect data from just about any webpage on the internet. We delight in partnering with our customers to build unique scraping solutions using our technology as the backbone. To get started creating your custom scraping solution, just send a message to our developers here. 

Our scraping service uses APIs and multiple proxies to allow you to collect data in real-time and from multiple pages simultaneously. With it, you can manage all the insurance data you need to build an insurance data analytics strategy.

Conclusion

A few weeks after I took out my first insurance premium, I found myself wondering if it was worth it. I never used to listen to my sister, so what changed this time? Then I remembered that I got a mail from my insurance company after tweeting about my changing views on insurance. Someone must have been watching. Or perhaps they used a tool like Scraping Robot to collect insurance data and target their customers better. You can do the same too. All you need to do is send a message, and we’ll help you take on the task of getting the insurance data you need when you need it.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

Saheed Opeyemi