Finding Insurance Policy Information on Insurance Company Websites

Anyone who has purchased an insurance policy online knows how difficult it is to compare them. This is because each company has different rates, and the information on different websites is presented in different formats. Browsing each company's site, finding the same data, and comparing it manually proves to be a challenging task.

That's why insurance data aggregators, which provide basic comparisons between insurance plans from different companies, collect insurance coverage information from provider websites. But they don't stop there; they also clean the data and then organize it by data fields, making it easier to compare and choose the most advantageous plan for the user.

Aggregators provide a great service to the general public by helping customers view information from different sources together. Whether it's news aggregators or insurance data aggregators, they help people find data in a single interface, which aids further in consuming data in various ways—such as associations or comparisons.

Benefits of Scraping Insurance Coverage Data

While we've discussed one scenario explaining the difficulties consumers face, data scraping of insurance coverage can have many benefits for different types of organizations. If you are an insurance company, you might even want to obtain data about various insurance plans from your competitors.

While some companies post data directly on their website's homepage, others may require filling out a form. Some may even require a subscription. For these reasons, collecting insurance coverage information from each of your competitors can be a challenging task.

Another important fact when it comes to insurance companies is their metrics—the percentage of people who successfully file insurance claims, the denial rate, and so on. According to government regulations in most countries, insurance companies must provide data on these metrics on their websites. But even then, finding this data is not always easy.

At the same time, data is often presented in the form of charts (which, again, are in image format), and these details further complicate the problem. However, collecting data related to denied claims and other metrics can help understand which companies truly support customers in times of need.

Nowadays, most hospitals have partnerships with major insurance companies. Deciding which insurance company to contract with can be another difficulty, and choosing the wrong company can lead not only to losses but also to a loss of customer trust.

That's why not only individuals and insurance companies, but even hospitals should collect and consume data from various insurance companies to understand which diseases are covered by which companies, so they can enter into agreements with one or more companies, ensuring that their patients can maximize their insurance plans and receive treatment without worrying about a large bill.

Challenges You May Encounter

When collecting data from various insurance companies, a number of difficulties can arise. In addition to those mentioned above, the most common problem is that companies constantly make changes to their websites to improve user experience.

Another issue is that many insurance companies post only part of the insurance coverage information on their sites. Fine print and deeper details are only available in PDF format (or even as images) on their websites. The problem is that while you can obtain PDF files from websites, extracting textual data from PDFs requires the latest OCR (Optical Character Recognition) software.

Scraping data from various websites is meaningless without proper mapping. Unlike e-commerce sites, where product data is placed on product pages, you have no idea which page (or set of pages) will contain insurance coverage information. In such a scenario, mapping specific web pages to data points will be crucial, and these mappings will also need to be updated as the website itself is updated.

How to Collect Insurance Coverage Data

Languages like Python have simplified the process of collecting data from websites, and scraping data from individual web pages has been described in many of our articles, such as this one. Thanks to existing code snippets that can be reused by programmers (called packages) and a more gentle learning curve, writing your own code to collect data from a single website displaying some insurance coverage-related data is straightforward.

However, if you want to build a scraping engine for commercial purposes to collect the latest insurance coverage plans and their details for a list of companies, it's better to create your own data scraping team. At ESK Solutions, we provide comprehensive solutions so you can simply connect a data scraping framework to your existing business logic and leverage the collected data to your advantage.

Conclusion

Data is king, and companies constantly use data in every possible way to grow and remain attractive. Some engage in data scraping, some even buy it, but ultimately data-driven decision-making is a necessity of our time. At this stage, a constant stream of data from the Internet is a blessing, and whatever is not scraped remains uncollected data.

And if you are not collecting data and leaving more data on the table for competitors to consume, you put your business in a vulnerable position—another blockbuster that will be consumed by Netflix.