How Big Data Is Revolutionizing the Insurance Industry
The digital technology revolution has transformed the professional world in significant ways. And it’s no surprise that big data technology has gained popularity, especially with the amount of insightful data that’s available to organizations.
In this article, we’ll cover:
- Definition: What is big data anyway?
- Characteristics of big data (The 4 “V’s”)
- The importance of big data
- How does big data work? (The 5 steps)
- Big data in the insurance industry
- How are insurance/insurtech companies using big data now?
- What does the future of big data look like?
Big data technology analyzes, processes and harnesses the data available to an organization in meaningful ways. After all, data isn’t valuable if it can’t be used to provide helpful insights, right?
And the insurance industry has not been left out of this big data revolution.
What is Big Data?
Businesses are flooded with data from different sources on a daily basis. And it shows: As of 2018, about 2.5 quintillion bytes of data were created daily. And these big data statistics have risen at an alarming rate ever since.
Big data is used to describe very large structured and unstructured data sets. Their complex nature makes them impossible to capture, curate, and process using more traditional software tools. Instead, big data typically requires more ingenious techniques and technologies with improved forms of integration to deliver insights and information.
When analyzed, these incredibly diverse, large, and often complex data sets can reveal trends, patterns, and associations that can be applied to human interactions and behavior.
While the idea of analyzing data within the insurance industry has gained popularity more recently, it’s actually an old concept. Before the word “big data” was ever mentioned, businesses were making use of analytics to reveal insights, patterns and trends – as far back as 1950.
However, big data really gained traction in the early 1990s. And although no one knows who first used the term, big data eventually became a trendy term thanks to John R. Mashey.
What are the Characteristics of Big Data?
Data must fall under the 4 “V’s” to be classified as big data:
1. Data Volume:
The size or quantity spells out the data’s value and associated insight. This is a significant part of determining whether or not the data is classified as big data.
2. Data Variety
Variety considers the type and nature of data. It can either be structured data or unstructured data. This classification helps the data analyst to effectively harness and draw insight from it.
In other words, big data can be in the form of texts, audio, images, or videos.
3. Data Velocity
Velocity is the speed at which data is generated and processed. And unlike small data, big data is generated more often.
With the rise of the Internet of Things (IoT) and the digital revolution, data is usually generated in organizations at an alarming rate. And it’s most readily available in real time.
Two kinds of velocity considered when dealing with big data are:
- The “frequency of creation/generation” and
- The “frequency of handling, recording, and publishing.”
4. Data Veracity
Veracity refers to the quality and value of data. And the quality of captured data can vary. When this happens, it can hamper accuracy in analysis.
What is the Importance of Big Data?
The significance of any data is within the insights it reveals.
When analyzed, big data can give information about new product development, optimized offerings, cost and time reductions and efficient decision making.
When combined with analytics, big data can be used to solve operational and business-related challenges.
How Does Big Data Work?
The central idea behind big data is making better decisions through analyses. This principle can be summarized under 5 steps, which encompass structured data, unstructured data, and semi-structured data.
The 5 steps are:
1. Set a Big Data Technique/Strategy
A big data strategy is a plan that helps to oversee and improve overseeing the way data is acquired, managed, stored, and transmitted within and outside the organization. This strategy is the foundation for business success amidst a large stream of data resources.
When trying to formulate a strategy, it’s necessary to consider existing and future technology and business goals.
Setting a feasible big data strategy will require the business to handle big data as they’d handle their other valuable assets – rather than simply a byproduct of technological advancement.
2. Locate the Big Data Sources
Big data is available to businesses from in the form of many different sources, including:
- Social media data
Social media data comes from engagements and interactions on channels like YouTube, Facebook and Instagram in the form of images, videos, and texts, as examples. Social media data can be used in sales and marketing functions.
- Streaming data
Streaming data comes from the Internet of Things (IoT). It includes several other connected devices that find its way into IT systems: Think smart cars, medical devices, wearables and more.
- Publicly available data
This includes a plethora of open data sources, including but not limited to: the U.S. government statistics, the European Union Open Data Portal and the CIA World Factbook.
- Other big data sources
Data from cloud data sources, data lakes, customers, and suppliers.
3. Access, Curate and Store the Data
Modern technology tools provide the power, speed, and flexibility needed to access large volumes and types of big data.
Beyond the ability to access this big data, organizations must also develop a technique for managing and storing the data while making sure the quality is not tampered with.
Data can be stored onsite at a business in the form of data warehouses.
But a more flexible, safe, and cost-efficient method would be to handle and store big data using cloud solutions. This is because cloud usage ensures that data can be retrieved quickly and that no data is lost in the process of handling.
4. Analyze the Data
Organizations can decide to make use of all their big data for analysis. They can then choose to filter through and determine which is most relevant to the organization.
5. Take Data-driven Actions to Make Decisions.
Accurate data means accurate analysis, which leads to effective and efficient business decisions.
To compete favorably in a fast-changing business environment, organizations and businesses alike must know how to sieve and explore the opportunities provided by big data and big data management technologies and applications.
Big Data in the Insurance Industry
Insurance companies might especially face many data challenges. But technologies developed by insurtech companies have helped insurance companies to overcome these obstacles.
Big data technologies fall into this category. This technology has transformed the software products and tools used by organizations in processing and managing their data.
Within the insurance industry, the improved technological applications of big data have allowed insurers to respond to the demands of their customers and develop more personalized products.
3 examples of using big data in the insurance industry include:
1. Big Data and Risk Prediction
Insurance is mainly about managing risks of all kinds. And the higher the risk, the greater the cost attached to it. Previously, insurance companies calculated these risks with internal data using manual methods.
However, due to the increase of generated data, this has changed. Data is now available from a wide range of external sources and can be generated on an ad hoc basis.
These improvements have provided insurers with more data to work with, leading to more efficient underwriting and smarter solutions.
2. Big Data and Fraud Detection
Big data includes prompt action elements that take effect right when the data is analyzed and can therefore enable the easy detection of fraud through the use of fraud analytics. Big data technology also detects duplicate items and removes them to ward off redundancy.
3. Big Data and Machine Learning
We’ve already covered how an overwhelming amount of data is available to insurers.
This causes insurance companies to face the dilemma of having to sift through it all to make the most ROI and glean valuable insights.
And quite honestly: This process could be too much on humans.
Cue machine learning. The introduction of machine learning has helped insurance companies to process data smoothly and efficiently at a faster rate and with improved accuracy. And machine learning can be applied to both historical and future data sets to improve the company’s operations.
Insurance data, when combined with machine learning, can also be used to improve pricing strategies, claims processing, and promotional content.
How do Insurance/Insurtech Companies Use Big Data?
The health, property & casualty (P&C), and life insurance sectors are just scratching the surface of big data benefits that can improve their business models. Let’s look at big data applications in the insurance industry.
Here are 4 ways insurtech companies are using big data in insurance:
1. Predictive Analytics
The insurance sector has been successful in forecasting risk and attaching compensation to it via predictive analytics.
Predictive analytics uses the big data collected by insurance companies to precisely and accurately calculate claims, pricing, emerging trends and risk selection.
According to a survey carried by Willis Towers Watson, more than 90% of P&C insurers agree that models have had a positive impact on loss ratio, rate accuracy, and profitability.
Insurance and insurtech companies that have chosen to adopt predictive analytics in their operations have noticed a positive impact, which leads to accurate and responsive processes – something our fast-paced and fast-changing market demands.
2. Big Data in Car Insurance
Major car insurance companies, such as AXA and AIG, are using big data to gather driver behavioral analytics to create specialized and customized insurance packages/policies.
- AIG offers drivers an internet-enabled application to score and track driving performance. AIG then analyzes this data to monitor patterns, create tailor-made insurance packages, and ultimately, offer better services to their users.
- AXA offers a “DriveSafe” application to drivers who are under the age of 24. The application records drivers’ journeys and measures their performance. AXA then uses the data to set insurance discounts for drivers.
3. Big Data in Health Insurance
Big data has delivered many benefits to the healthcare world. For health insurance companies, this healthcare technology gives access to a large stream of data, helping companies to provide premium programs and offers to their customers.
As an example, HumanAPI is a platform that facilitates real-time access to healthcare data from a variety of sources. The company uses insurance big data to create more accurate pictures and forecasts of health patterns. This ultimately helps HumanAPI to develop tailored health insurance policies for customers.
Big data analytics in healthcare can also save costs and prevent future fraudulent activities in the health sector. In fact, the Center for Medicare and Medicaid Services (CMS) saved more than $210 million in fraudulent health claims with big data.
4. Big Data and Artificial Intelligence (AI)
Machine intelligence and Artificial Intelligence (AI) have been around for some time, with massive investments from companies like Google, Facebook and Microsoft.
Forward-thinking insurance companies have also latched on to big data and AI as the next big thing in the industry. AI works to identify emerging risks and trends for businesses and individuals alike.
What Does the Future of Big Data Look Like?
Here are some rising big data trends we’ve seen more recently.
1. Big Data and Telematics
Telematics uses sensor technology to retrieve and pass on real-time data over long distances. And it’s rapidly taking over the insurance space.
Many insurance policyholders allow insurance companies to analyze their data and behavior through things like wearables and vehicles (much like what AXA offers to its users in our example earlier on).
According to a recent study, the usage rate of personal telematics in insurance policies rose from 1.5% in 2015 to about 10.3% in 2020. This shows a major increase from 12 million users to about 90 million users by the end of 2020.
The most exciting part is how telematics influence customer behavior. Since consumers know that their movements are recorded and scored, they become more aware of healthier behaviors and drive in a safer manner.
This, in return, saves insurers a substantial amount on claims processes.
2. Big Data and Internet of Things (IoT)
Another fast-emerging big data trend is the rise of the Internet of Things, IoT.
Thanks to IoT, we now have a groundbreaking amount of data generated daily. In fact, it’s estimated that about 2.5 trillion quintillion bytes of data are generated every single day. This implies that about 90% of the world’s data has been created in only the last two years.
IoT has delivered significant improvements in the insurance technology sector. For example, insurtech companies use IoT to capture and transmit data over the internet.
3. Big Data and Artificial Intelligence (AI)
AI enables organizations to use big data for more valuable actionable insights.
A recent study by IBM showed that AI would improve data analytics in insurance by optimizing and integrating processes, increasing speed, and generating new insights.
Big Data Study Conclusion
Big data technology has created an endless stream of opportunities, which have resulted in an increase in its usage – especially in the insurance and insurtech industry.
Overall, big data in the insurance industry has helped businesses make processes more efficient, save on costs, predict behaviors to develop better products, and increase productivity.
And big data technology isn’t going anywhere anytime soon.