In today’s time, it is imperative for every sector to focus not only on better data handling solutions but also steadfast the use of the data that is long untamed. The findings of a recent study indicate that Data-driven organizations are 23 times more likely to acquire customers than their peers. As per a recent Worldwide Analytics Spending Guide released by IDC, businesses are spending $187 billion on data analytics in 2019.
Insurance is a data-driven industry. There are now so many players in the competition and each one of them has a mine of data but only the ones converting that data into useful insights can make it a gold mine. According to the findings of a recent study, 86% of insurance companies are working on Insurance data analytics mechanisms for optimum predictions of big data reports.
This is the power of data that is being used as a source of energy today. But this source of data needs to be unleashed to its full power by procuring insights that will help the insurance companies achieve their long term goals.
The first step of insurance companies in utilizing the strength of the data available is by mapping their long term goals. Different organizations can aim for growth in different aspects of insurance processes like for mitigating their routine challenges, automating complex processes or achieving competitive advantage in the market. It could be for one of the processes or all of these.
If data analytics in insurance industry is leveraged appropriately, it can enable transformation for the Insurers. This blog will further delve into the benefits of Data Analytics for the Insurers.
How Data Analytics Benefits Insurers?
1. Generation of Leads
In the age of competition, every insurer is facing difficulty competing with the internet. In this scenario, the unstructured data available on the web is an unchained source of lead generation. Insurance data analytics of such unstructured data provides you a deep dive into the customer behavior and market opportunities to up-sell and cross-sell.
2. Enhancing Brand Value by Improving Customer Satisfaction
An insurance company that can rightly predict the needs of the prospective customers looking through data trends has much more potential to make the sale than an insurance company just using conventional methods of selling. Analysis of the existing customer data can also offer prescriptive insights in improving customer satisfaction.
3. Reducing Fraudulent Cases
The Insurance sector suffers by challenges of fraud cases in claim processing. This is mitigated by predictive analysis in insurance industry. For example, the fraudulent cases already taken place are stored in the data trends of an insurance company and while processing any claim, the insurers can carefully check if the trend is repeated. This, in turn, helps reduce the act of fraud.
4. Predicting Accurate Risk for Underwriting
Underwriting is a complex task for the insurers which can be simplified through Insurance data analytics. For example, the data trend would predict a higher premium for a customer who has been engaged in rough driving than that of a customer whose data trend predicts a lesser risk profile.
5. Enabling Business Growth
One of the important elements of the Insurance domain is quantifying the levels of risk which is best done by procurement of analysis of useful data available. In this way, Insurance data analytics acts as an engine to the growth of Insurance companies with its capability in predictive analysis of big data.
The emerging leaders of the insurance sector are taking the right advantage of insurance data analytics in their decision making processes of pricing strategies and risk selection. The new-gen technology is working progressively for deploying prescriptive methods of procuring deep insights from the big data in various insurance-related transactions like underwriting, claim management, customer satisfaction and policy administration to ensure better predictive analysis. This enables insurance companies to portray analytical decision making in all their internal processes and business transactions.