Big Data for Better Insurance

November 28, 2016 Avani Desai

Orignally published at www.insuranceciooutlook.com

Based in China, ZhongAn Online P&C Insurance Co. (“ZhongAn”) has taken the insurance world by storm by offering a series of unusual and unique insurance policies, such as those aimed at supporters during the 2014 World Cup by insuring against self-inflicted liver damage. Founded in 2013, ZhongAn is still an infant in the world of insurance, however, being innovative in their approach, they are already valued at around $2 billion.

The insurance industry is, like most of our world commerce, entering a new era known as “the fourth industrial revolution” or “Industry 4.0”. This new era is built on new technologies, such as the Internet of Things (IoT) and big data. Innovative, forward thinking companies like ZhongAn are capitalizing on the advances in the industry and reaping the rewards. Moreover, much of this innovation centers on big data and its usability.

Data has always been the central pivot upon which the insurance sector works, and big data is effectively moving that pivot. The power of big data is being used in the insurance sector as a transforming movement. With analysts like IDC predicting that big data and business analytics revenues will be valued at $187 billion by 2019, the world of insurance is already on top of this, creating new products and services, and enhancing existing ones by using the big data movement.

How Big Data is Transforming Insurance

The insurance industry is fundamentally about understanding and managing risk. The more information you have about any given area of life, the more you understand the risks behind that area. Insurers have always used data to predict outcome and set a price on a policy. Previously, this was done in a more manual manner by using internal data. Now, data is used from multiple external sources and generated ad-hoc. Also, the U.S. government has recently made government statistics publicly available through their ‘open data’ initiative. This provides insurers several layers of data to add into the big data equation, allowing them to implement smarter underwriting.

“Data has always been the central pivot upon which the insurance sector works, and big data is effectively moving that pivot”

Pooling multiple external data sources is one thing, but having deep insight into the dynamics of these data is key to actually making full use of it. This is where big data analytics comes into play, being as important as the data itself.

Big Data Examples Within the Insurance Industry

One of the best ways to look at how big data analytics is impacting the insurance industry is through examples. There are some insurance areas that are being used as early adopters of technology in the big data insurance revolution. For example:

Car Insurance: The car industry and auto insurance is an area that insurance companies are focusing in on. Some insurance organizations across the world, including AIG, Allianz, and AXA, are using driver behavioral analytics to create tailored insurance packages. 

AIG offers drivers an Internet-enabled app called ‘XLNT Driver’, which records driving performance and scoring you on journeys completed. It, then, shares these data with AIG in the Cloud, allowing them to create tailored insurance packages, ultimately offering a better insurance package and encouraging safer driving.

AXA offers the ‘DriveSafe’ app for drivers under 24. The app records journeys and shares journey data through an Internet-enabled app with AXA. The data generated by the app sends AXA journey information showing your ability to drive safely; for example, keeping within the speed limit. Using these scores, AXA can, then, offer insurance discounts for drivers.

Both examples above work on the principle of sharing data for something back—i.e., creating value for the consumer. This principle is borne out by research that is carried out by Pew Research, who found that data sharing was deemed acceptable if a ‘deal’ was in place, such as saving money.

Health Insurance: Healthcare has been an early adopter in the big data space.The healthcare data analytics market is projected to be worth $24.5 billion by 2021.With all this big data being drawn in from a myriad of health devices, including IoT devices such as health wearables, this data are well-positioned to be utilized by the insurance sector offering premium discounts for active people. Developments in big data analytics, coupled with increasing healthcare costs associated with increased fraud and abuse, have created the perfect storm for the healthcare insurance industry, encouraging personalized health insurance products.

The use of big data analytics in healthcare is already showing great success. Big data analytics was the reason behind a saving of over $210 million in fraudulent health claims. The Center for Medicare and Medicaid Services (CMS) used big data analytics to identify inappropriate payments using analytics algorithms to analyze billing patterns.  Additionally, platforms like the HumanAPI, which facilitates real-time access to healthcare data from a myriad of sources, healthcare (and insurers within the industry) will have access to even more datasets, building more accurate pictures of health patterns, allowing in turn more accurate personalized health insurance policies.

Big Data and AI in the Insurance Industry

Artificial Intelligence (AI) and Machine Learning is not a new concept, as it has been around for several decades. But it has made progress in recent years with the likes of Microsoft, Google, and Facebook, all having dedicated AI labs. AI and big data are being hailed as the next generation insurance tool for predictive analytics. Insurers like Zurich and AIG are already on the AI bandwagon expecting AI to augment and improve on big data analytics. AI and Machine Learning open up even more opportunities in insurance to collect data and analyze it in a smart way. In a recent interview with George Argesanu, from AIG, he said that “Machine Learning will enable us to “see” and hopefully, prevent an accident before it happens by recognizing the patterns in the driving behavior, traffic, and road conditions.”

PwC, in their white paper AI in Insurance: Hype or Reality?, commented on the use of AI in insurance stating that “AI’s most profound impact could well result from its ability to identify trends and emerging risks, and assess risks for individuals, corporations, and lines of business.”

With insight such as that described above, insurers will have a powerful tool in AI for creating even more accurate and individualized products, and in doing so, reducing their risk.

Revolution, But With Evolution–The Security Conundrum

Big data is offering some advantages to the insurance industry. Big data offers insight into customers’ habits, allowing insurers to make more accurate risk predictions based on individual behavior patterns. Having more data on patterns of behavior also means that fraud detection is more accurate. Ultimately, understanding and applying behavioral analytics, based on very large sample sizes, results in a more seamless way of managing the lifecycle of the claim.

But, while we celebrate this achievement in bringing positive disruption to the insurance industry, we must keep in mind that these data that we collect can be a double-edged sword. Data is power in the world of cybercrime; big data may reduce the risks associated with insurance, but it can increase the risks associated with privacy and cybersecurity. Data being shared via IoT devices and with Cloud-based services, unless correctly secured, is open to exposure. This aspect of big data in insurance should not be overlooked and should evolve with the use of big data. As custodians of this data, the value adds regarding personalized policies and reduced costs should also be complemented by commitments to the privacy and security of that information.

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