How Big Data is Transforming the Insurance Sector
When we highlighted new technology trends in the insurance sector, we said that investment in insurtech had not stopped rising since 2018. Among the reasons were changes to capture predictive data in real-time. Thanks to this, big data is a very prominent part of the insurance sector.
In 2019, insurance business advisor Willis Towers Watson claimed that more than two-thirds of executives surveyed in the Americas believed predictive analytics had helped them cut costs, while 60% believed the extra insight was helpful in growing sales and profitability.
In this article, we share all the reasons why big data is a must-have for insurance companies. Thanks to it, your business will be boosted and will know new directions.
Big Data Applications for Insurance Companies
As we saw with the Willis Towers Watson study, big data in the insurance industry is highly effective for predictive analytics. Data collection can predict customer behaviors, such as whether they are about to cancel or take out insurance. It can also be used to set prices by analyzing competitors’ prices or what consumers would be willing to pay.
In addition, as we have seen in other posts in which we talked about the benefits of big data, big data can also be used to save costs in some departments or optimize processes to be more productive: data analysis and visualization improve accounts and make work more efficient.
Detecting insurance fraud is another of the benefits of big data in the insurtech sector. By analyzing customer and claims profiles, it is possible to determine a possible case of fraud and therefore stop it early. Some also argue that the data can be used to find suitable law firms to litigate fraud cases if they go to court.
But at the same time, big data serves the opposite purpose: to train a tool with which to calculate claims following accidents, which are indeed real. The patient’s health data also helps to calculate a policy before selling it and thus reduce possible future healthcare costs.
Data: Open Source, Internet of Things, and Social Networks
When we were talking about new technology trends in the insurance sector in 2022, we also mentioned that open-source protocols would appear for data to be used in many industries. Also, that users could give data collected by their connected devices to the Internet of Things (IoT). According to Markets & Markets, the global IoT insurance market would have a market value of $42.76 billion by 2022.
Another place to pull data from can be social networks. A place where consumers praise or criticize a product. Collecting that praise and complaints can be used to improve existing products or create new ones.
Big Data Analytics in Insurance
As we have seen, big data analytics in insurance is very important. To execute a big data strategy, having cloud servers is the best idea: it collects more data and processes it faster to get analysis sooner.
Cloud computing improves the performance of analytics in real-time and in greater depth. It also speeds up machine learning processes. Thanks to this there is also greater personalization of products and services: premiums are tailored in real time to literally any customer. This results in better customer experiences and more personalized marketing campaigns to attract new audiences or build loyalty among existing ones through policy renewals. Because that is another major trend in the use of big data in the insurance industry: big data also serves to predict who is most likely to cancel their insurance and reach out to them to offer them a product of their interest.
In short, the benefits of big data in insurance can benefit both customers looking for good products and insurers looking to reduce fraud and wasteful spending while offering better services to their audience. If your company is interested in knowing the trends in policies, how to attract customers and build customer loyalty or change the business according to the needs of society, we work with you to get from the big data all the sense that we do not see in words and figures in isolation.