In the insurance industry, big data is the name of the game. It provides 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 2020, analytics in insurance 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 is simply not 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 about. With machine learning capabilities, insurance data analytics solutions can process at a higher rate of speed with improved accuracy and efficiency. Machine learning can be used retroactively on historical data sets that insurers already have, as well as proactively to discover new ways to improve operations. With machine learning, insurance data can now be used to improve:
- Pricing strategies
- Promotional content
- Claims processing
- And more
2. Predictive 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
According to a Willis Towers Watson survey, over 90% of P&C insurers say models have had a positive impact on rate accuracy, loss ratios and profitability. So, the results from the widespread use of predictive analytics in insurance have resulted in more accurate and more expedient processes across operations. And in today’s fast-paced market, carriers can’t afford to take it slow.
3. 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 in the European Union in 2018, 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.
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:
- Risk assessment
- Marketing campaigns
- Claims processing
- Claims leakage
- Product pricing
5. 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.
6. Artificial Intelligence
AI has been disrupting the insurance space in the ways that insurers handle claims processing, underwriting, and even customer service. However, AI is also a valuable tool in the big data space of insurance operations, in that it acts as the central power hub, driving powerful automated tools like machine learning and predictive analytics that make digital insurance capabilities possible.
AI takes all of the data generated in today’s market – from online behaviors to telematics to historical data – and turns it into actionable insights. A recent IBM study outlined a big picture look at how AI will improve big data analytics in insurance, including:
- Increasing speed
- Optimizing processes
- Generating new insights
7. Increased Data Availability
It’s a widely-accepted fact that P&C insurers have an unfathomable amount of big data at their disposal, and that this limitless data is helping improve accuracy and efficiencies like never before. However, with such an immense volume of data, how can insurers be confident that they are working with their data in the most efficient means possible?
This is what is known as data availability – products and services that ensure that data continues to be available at a required level of performance in situations ranging from normal through “disastrous.” To be competitive in today’s insurance industry, carriers must have systems in place to ensure high levels of data availability at all times. This level of speed is what enables the other advanced functions in the insurance data space, such as machine learning, predictive analytics, and IoT.
8. Blockchain Data
Blockchain – the unique, hyper-secure data system that made Bitcoin possible – is now beginning to transform the insurance industry. Blockchain data is “virtually incorruptible” due to its construction. In an article detailing its impact on the insurance industry, Forbes describes blockchain data as data blocks that “link to a previous block and have a time and date stamp” that cannot be altered.
The full impact of blockchain data in insurance analytics is still to be realized, but the potential is multileveled. At the most basic level, it will enable the even more secure exchange of data between customers and insurers, improving efficiencies and transparency. A recent report from PwC highlighted multiple other uses of blockchain in big data insurance analytics, which include:
- Building apps around risk assessment, saving an estimated $5 billion to $10 billion dollars annually
- Faster claims processing and payment to customers
- Reductions in claims leakage and fraud, saving 15-25% for insurers
- Improved accuracy and legitimacy of data, meeting legal standards
Telematics – the use of sensor technology to collect and transmit real-time data over long distances – is the latest trend in data collection and the insurance space. People have begun to opt in to plans that analyze data from their wearables and automobiles to better inform their insurance policies, in hopes of earning cheaper premiums.
A new study forecasts the usage rate of personal telematics insurance policies is estimated to increase from 1.5%, as of December 2015, to 10.3% in 2020 – representing an increase from 12 million users to nearly 90 million by the end of 2020. The most interesting takeaway from widespread telematics use, though, is its ability to profoundly change customer behavior. Because their movements are being tracked, consumers are more apt to drive safely, in turn saving insurance companies substantially on claims processes.
How to Leverage Big Data in Insurance
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 the best use of these tools are best equipped to give customers the experiences they expect, while transforming their business practices with next-generation big data technology.