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4 Types of Analytics Defining the Future of the Insurance Industry

Posted on August 16, 2016 by Guy Weismantel in Digital Insurer

vertafore big data analytics predictive descriptive diagnostic prescriptive

The 2015 Global Insurance Outlook report published by professional services firm Ernst and Young states the word "technology" sums up the focus of today's insurers.

As the leader in modern insurance technology, Vertafore is intensely focused on helping agents and carriers alike get the most out of today’s technology while simultaneously working with you to help define the future of insurance technology.

Research shows us big data and analytics are dominating the minds of insurance carriers as they strive to stay ahead of competition in today's insurance industry.

We’ve talked about what makes big data ‘Big’ and how access to SO much data can leave us feeling overwhelmed. But when data analytics are divided into these four categories, understanding what is possible for your agency becomes much more manageable.

Through descriptivediagnostic, predictive and prescriptive analytics methodology, insurance firms can use all the data available to them and in turn, make their business more efficient while providing better care for their customers.

Let's take a look at what each type of analytics means and, more importantly, how each can help independent agents, and its carriers, and the insurance industry as a whole.

Descriptive Analytics and Insurance

Descriptive analytics consist of any results capable of being analyzed and synthesized to further benefit a business -  such as page views and web activity, social interactions, blog mentions and more. According to Information Week, more than 80 percent of analytics business deal with are descriptive, and this is where insurance carriers start in their analytics journey.

An example of an insurance provider using descriptive analytics to directly benefit their clients is Progressive’s Snapshot. The tool helps determine car insurance pricing based on the driving of their customers. This "usage-based insurance" works after customers plug a small device into their vehicles, which monitors actions such as total number of miles driven, sudden changes in speed, and total high-risk driving time. The device beeps when the driver makes a hard brake, which factors into the insurance pricing regarding number of hard brakes made per 100 miles, and also makes drivers safer in order to reduce the number of claims made.

You’re probably familiar with the tool, but the video below is very data focused and does a great job showing off Progressive’s big data capabilities. The fact that you are even aware of Progressive Snapshot is a testament to how ubiquitous these big data powered tools are becoming.   

Diagnostic Analytics and Insurance

Once insurance companies find the raw data that powers descriptive analytics, the next evolution in the analytics journey is turning those into diagnostic analytics. This means examining the data to answer the "why". A common form of diagnostic analytics is regression analysis, which can be used to estimate the relationships among variables, drill-down analysis to discover a cause, and deep data mining to discover correlations.

By analyzing the descriptive analytics in hundreds of thousands of homeowners' insurance claims, insurance carriers can identify which conditions cause a homeowner to be threatened more by theft and burglary. Insurance comparison site Compare the Market educates homeowners on when their homes are most likely to be broken into (weekdays between the hours of 6 a.m.- 6 p.m., when they're likely to be away at work), and how they can reduce their risk of theft around their homes. Based on details found in burglary and theft claims, homeowners insurance carriers can help their clients reduce the number of claims by alerting them to lock doors and gates, avoid keeping a spare key outside the home, and to keep their blinds drawn. Using diagnostic analytics allows insurance carriers to help their customers make intelligent and healthy decisions to reduced reduce and keep customers stay safer.

Predictive Analytics and Insurance

Predictive analytics find their power in their ability to maximize efficiency. Customers are happier when greeted by tailored, custom experiences. Employees are happier when they can provide customers with better service. And they help insurance companies save money by predicting future events and saving more time to plan accordingly.

Seeing the what and the why provided by descriptive analytics and diagnostic analytics allows insurance carriers to anticipate trends and pivot so they're prepared.

Healthcare insurance provider Aetna uses predictive analytics to cut down on the amount of fraudulent claims while also saving time and money. This compared with the high costs of pay-and-chase once a fraudulent claim is discovered. TechTarget reports Aetna uses retroactive analysis to find links between referring physicians and pharmacies to prevent the driving up of fraudulent claims.

Aetna also monitors claims to identify spikes that may indicate fraudulent behavior. This allows the company to predict the prevalence of individual fraudulent claims and stop payments from being issued. By using predictive analytics, insurance companies can maintain better control of fraud.

Does this seems a little too far reaching? Or does it sound like something your organization is doing today? I’d love to know in the comments section below.

Prescriptive Analytics and Insurance

By using all the above-mentioned forms of analytics to better understand their customers, the likelihood of actions, and the ramifications for those specific events, insurance companies can use prescriptive analytics to optimize their strategies and improve their businesses. Prescriptive analytics recommend one or more courses of action which can be measured and refined based on results. Prescriptive analytics are always evolving but are of crucial importance to put the rest of the work a company has done gathering and organizing data to its optimal use.

An example of how prescriptive analytics are used in the healthcare industry is when providers measure clinically obese patients and then use risk factors for conditions such as high cholesterol and diabetes to determine where to focus treatment. In insurance, property and casualty insurance providers can use catastrophe modeling price plans to recommend ways to set pricing, establish policy conditions, and optimize portfolios to keep accumulation of risk in check. As more providers increase their offerings in relation to threats such as cyberattacks and natural disasters based on climate change, prescriptive analytics powered by data can even shape the future of the insurance industry.

Big data requires thoughtful strategy in order to make sure companies and industry professionals best utilize the information for their business and their customers. If you'd like to learn more about how the power of data analytics can help your own business, check out our free e-book for more information.

 

Guy Weismantel

Mr. Weismantel is the Vice President of Marketing at Vertafore. With 20 years of marketing and financial leadership in companies such as Microsoft, Business Objects, Baxter HealthCare, Caremark International, and Expedia, Guy’s career has focused on bringing differentiated products to market and providing the “compelling reason to purchase” for customers and prospects alike.


Guy has a Bachelor's Degree in Accounting from the University of Notre Dame, and a Master's Degree in Business Administration from the Kellogg School of Management at Northwestern University.

 

 
 

 

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