5 Ways Big Data Is Making a Splash in the Insurance Industry

5 Ways Big Data is Making a Splash in the Insurance Industry

There’s conflicting opinions on when the term Big Data really came onto the scene, but it is undeniable that its notoriety has skyrocketed in the past few years. Big Data has become the hot topic buzzword around the internet, as is made obvious in the Google Trends graph below.

One by one as analytics solutions became more accessible, industries found different ways to leverage the Big Data Revolution to their best advantage in their field. Virtually every industry, from manufacturing and retail to healthcare and education, has found a use for the ever-growing labyrinth of data resources.

The Insurance Industry is no exception in the Big Data Revolution. The Ernst and Young 2015 Global Insurance Outlook claims “technology” is the one word that encompasses the Insurance industry right now. The report states, “Insurers across all regions are capitalizing on data analytics, cloud computing, and modeling techniques to sharpen their market segmentation strategies, reduce claims fraud and strengthen underwriting and risk management.”

Although there are many ways that Insurers are utilizing Big Data and data analytics to benefit their bottom line, there are 5 areas being impacted the most.

1. Data for streamlined underwriting

Insurance underwriters are faced with the daily challenge of providing policy recommendations that are both fair to the consumer, as well as protect the best interest of the insurance company. Big Data is changing the future for underwriters as they must adapt to their new roles as data analysts as well as underwriters.

A study by Marketforce, the Chartered Insurance Institute (CII) and the Chartered Institute of Loss Adjusters in conjunction with Ordnance found that most underwriters are taking the evolution of their industry in stride. The study revealed that 9 out of 10 underwriters see the potential in access to real-time claims data to improve pricing accuracy.  Real-time data mining is streamlining the underwriting process by providing accurate, current insights that could have taken days to locate and consolidate. With access to quality data sources, underwriters are able to complete processes in less time and with better accuracy. Real-time data facilitates streamlined processes leading to higher placement rates and a much faster underwriting cycle.

2. Data for Personalized Policies

Access to multichannel data sources gives underwriters the ability to base premium costs and policy parameters on a more realistic view of risk as opposed to generalized assumptions based on factors such as location and age. Consequently, this integration of highly granular and individualized characteristics into the underwriting cycle is driving a more personalized consumer experience. Customers who feel they are receiving fair treatment instead of at the mercy of generalizations receive a more positive experience and are more inclined to remain loyal clients.

3. Identifying Customers at Risk of Cancellation  

Leveraging Big Data insights is well known for its ability to provide quality prospects for businesses, but another lesser known feature is its ability to shed light on low quality prospects or frustrated clients. Advanced analytics tools allow insurers to target individuals who are apt to be a long term loyal customer, and also to weed out individuals who are a high risk of canceling coverage. Predictive analytics is used to track and reveal signal behaviors that indicate an impending cancellation. This allows insurance agents to reach out to unhappy consumers before their final decision has been made, and tailor opportunities to encourage them to stay with the company. 

4. Identifying Risk of Fraud

Fraudulent claims are an unfortunately common occurrence afflicting the insurance industry. The Coalition of Insurance Fraud estimates that nearly $80 billion in fraudulent claims are made annually in the United States. This staggering statistic has led to heightened awareness and the use of predictive data analytics to detect applicants with a higher propensity to commit fraud. Additionally, after a claim has been made Insurers can use data mining to track digital and social channels for evidence of fraudulent behavior.

5. Customer Relationship Management

Data has become an indispensable tool for Insurers to implement positive consumer relationships and intuitive acquisition strategies. Delivering exceptional customer experiences through a comprehensive customer profile provides better understanding of customers’ preferences, lifestyles, and other key characteristics, which allow insurers to deliver highly relevant and personalized offers. By investing in Data Integration and Quality solutions, insurers can build a fully integrated marketing database that pulls data from various locations to improve customer intelligence and ensure a positive experience. Concerning acquisition, insurers use DaaS to monitor  behavior in real-time to target high quality prospects.

One example is a national life insurance company who knows that 41% of life insurance purchases are motivated by life stage events. Armed with this knowledge, they used DaaS to examine potential data sources such as baby shower registries (arrival of a new child), wedding registries (inclination of customers to plan ahead for spouse’ sake), or various social signals that indicate retirement or improved financial state that places individuals in a primed mental state to receive advertisements on life insurance. Tragedies can also influence insurance purchases. The death of a loved one, a natural disaster, or nearby crime all encourage an interest in planning for the future.

Big Data is impacting all industries in different ways, but it’s certain that the insurance industry is one of the leading innovators of DaaS implementation. As industry leaders continue to invest in Big Data solutions it will soon become as ingrained into the insurance industry’s processes as underwriting itself.