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Insurance Big Data Analytics Trends in 2019: How to Leverage Your Data

In the insurance industry, big data is the name of the game. Insurance big data analytics gives valuable insights into all facets of company operations and performance – from consumer behavior to underwriting practices to the ROI of marketing campaigns. Companies that want to leverage that information into actionable insights turn to big data analytics.

In 2019, insurance big data analytics will be more than just crunching numbers. Trends indicate we could see new strategies for insurance big data analytics that will help companies do even more with their information. Here are some of the latest trends in big data for insurance, and how you can use information you can already access to get ahead of your competitors.

Trends in Big Data for Insurance

1) Machine Learning

One of the most pressing issues for insurance companies today is how to most efficiently and accurately sift through the troves of data they collect. There simply isn’t enough manpower or hours in the day to maximize the ROI on insurance data and glean actionable insights from that information.

That is, until the advent of machine learning came along. With machine learning capabilities, insurance data analytics tools can run at a higher rate of speed with improved accuracy and efficiency. Not only can machine learning be used on historical data sets that insurers already have, but their power can also be used to be proactive in business operations. With machine learning, insurance big data analytics can now be used to improve, among other things:

  • Pricing strategies
  • Promotional content
  • Claims

2) Data Privacy

While insurers have massive amounts of data at their disposal, new laws and regulations are changing how insurance companies and their analytics teams can operate.

The General Data Protection Regulation (GDPR) became law last year in the European Union, triggering a worldwide examination into consumer data protection. The GDPR outlines what consumer data can be collected and how, and similar laws have been passed stateside. All 50 states have some sort of data protection regulation, with the strictest data laws in California and Vermont.

It is vital that insurers utilize a data system that is flexible and scalable, so they can stay in compliance as laws and regulations continue to change over time.

3. IoT

The need for more data security and regulation is largely due to the vast amounts of data we now have access to. With the creation of the Internet of Things (IoT), we have created virtually incomprehensible amounts of data – 2.5 trillion quintillion bites of data are now generated every day. To put that into perspective, 90% of the world’s data has been generated in the last two years.

The IoT and its role in big data analytics in insurance is essentially limitless. It gives insurers access and insights unlike anything they had before, and can impact all areas of business. This year, IoT insurance data will be used to improve, among many things:

4. Unstructured Data

The most common data used in insurance analytics is what is known as structured data. This data is what is volunteered directly by consumers, like name, address, gender, age, etc., that might be entered into standard forms and tables. This data is easy, accessible, and usable, but it doesn’t paint the whole picture. The new frontier for insurance data analytics is unstructured data.

Unstructured data includes things like social media data, multimedia, or written reports. New technology, like the IoT, has created a method for unstructured data mining and analysis, creating an even more robust profile of customers and consumers. Social media data has even been used in insurance fraud detection and for communicating with customers. Big data that encompasses this info contains a major, formerly missing piece of the analytics puzzle.

How to Leverage Insurance Big Data Analytics

The insurance industry as a whole is dependent upon forecasting risk and reward, and one way many insurers do that is with predictive analytics. Predictive analytics takes the big data collected by insurers and uses it to most accurately and precisely calculate, among other things:

  • Pricing and risk selection
  • Claims triage
  • Emerging trends

Having good data is one thing; knowing how to maximize its usefulness is something else entirely. There are various platforms, tools, and strategies that insurers can use to make the most of their data, but no matter the approach, carriers must be able to collect, manage, analyze, and report on this data quickly and accurately.

The insurance industry now requires absolute speed and accuracy, especially with data. Carriers looking for big data analytics solutions need platforms and tools that afford them time for analysis by eliminating the time dedicated to collection. The insurers making best use of these tools give customers the experience they expect, while transforming their business practices with next-generation big data technology.

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