In computer language, scraping a website consists of absorbing all or part of its content. The market of insurance companies is wildly keen on it… but it remains discreet because the practice of web scraping is not always legal everywhere.
If data is the new oil, then the Internet is an open pit of black gold. The websites are full of easily accessible information … everything is just a click away. So why deprive yourself and not help yourself to this gigantic digital treasure? This is what web scraping allows. This computer technique consists of sucking up data published on websites using automated queries.
The insurance industry is wildly keen on this practice, which absorbs large amounts of data without having to spend a single cent. Quiet! We shouldn’t brag about it too much. Because to indulge in web scraping is to expose oneself to legal proceedings for violation of intellectual property or non-compliance with contractual obligations.
4 uses of web scraping in the insurance industry
As we already know, if an industry has traditionally used data for decision-making, it is undoubtedly the insurance sector.
Although we are already used to the existence of professionals specialized in big data or business intelligence (BI) in the insurance companies in the USA, even today, this use of data is limited in some organizations to the exploitation and use of combined internal and historical data with other standardized data sources that most companies have access to.
This situation leaves insurer data analysts with limited and biased raw material to work on. Web scraping is a handy tool to feed insurers with data and provide them with a 360 view incorporating external data from existing sources.
1. Dynamic pricing based on competition
Until relatively recently, insurance policy premiums or insurance plans were kept almost secret.
The appearance of online comparators, together with the consolidation of the Internet as a distribution channel (the vast majority of companies already have online pricing), has led to more transparency in the sector’s prices. And with transparency, the price war has been favored.
The complex pricing of the different types of policies requires technology intervention to obtain lists of rates for the different possible variables. A bot crawler can access a company’s website and fill in pricing forms with the other variables defined to obtain rates in every possible scenario.
Data scientists will work with this data to add the competitor’s price variable to internal pricing decisions or even to try to figure out your competitors’ pricing algorithms.
2. Market research
Competitor research is not limited exclusively to prices.
We have numerous sources from which we can extract data to monitor competitors and try to advance their movements and strategies. In a sector as competitive as the insurer, this takes on greater importance.
For example, we can track job offers posted on different portals to find out in which direction their strategies are going, monitor their marketing efforts and advertisements, follow press appearances, etc.
This is a task that is already carried out regularly but can be fully automated to save time and cost.
3. Alternative data
Alternative data is a type of data that we get from less traditional sources. We consider alternative data those that are not used by the majority of the sector.
They are especially important because if we manage to detect it and obtain them, they represent an added advantage to any strategy. We play with an added wild card that our competitors do not know.
As a general rule, these are usually more hidden data that require diligent efforts to obtain. In this particular case, the data analyst’s expertise takes on greater importance since the key is to identify which data will be relevant to the strategy.
4. Exploitation of disused internal data
In many companies, we find large data sets wasted due to inflexible legacy systems.
Data, in many cases, dispersed without a standard storage system. Does this cause a lot of delay for data scientists in processing to extract information from them?
Scraping and data mining allows us to unify, heal, and heterogeneous all these sets in an automated way.
Conclusion
That data is already the fundamental pillar for business decision making is not in dispute. Even though the insurance sector is one of those that historically have used data the most for decision-making, this use has been limited to internal data and a global vision, and it is also necessary to look outwards.
Although there are numerous sources from which we can obtain data, these are protected to make it difficult to access and use, and advanced techniques are necessary to get them. Tomorrow’s insurance industry leaders will adopt new technological advances and integrate accurate models based on data.
