The insurance industry is probably not the first area of human endeavor one thinks about when somebody mentions words like ‚Äúinnovation,‚ÄĚ ‚Äúartificial intelligence,‚ÄĚ and ‚Äúhigh tech.‚ÄĚ However, in reality, it is one of the first to embrace the change. And, if you think about it, it is only natural.
Insurance is, by definition, an industry built around risk. Insurance companies greatly depend on their ability to predict what risk this or that person, company, or organization represents. The more information they have about them and the more accurate this information is, the more likely they are to make a correct prediction, either saving themselves money or earning extra revenue. The emergence of AI and Big Data technology means that insurance companies should scramble for the ways of implementing them in their work to get the much-needed edge over the competition. But how exactly do these innovations change the industry? Let‚Äôs take a look at some of the most important examples.
One of the most obvious examples of insurance industry technology that completely changes the way things are done are telematics and wearable sensors collecting information about customers. For example, if such a device is installed in a car, it gathers information on how the customer is driving: how fast he goes on average, how quick he is to accelerate, how she brakes, whether he is likely to go over the speed limit, and so on. All this information allows the company to build a comprehensive image of the client as a driver, indicating how likely he is to become a cause of an accident and thus how risky he is as a customer.
Currently, financial models are mostly built based on statistical samplings of past performance ‚ÄĒ that is, companies study the client‚Äôs record and build their predictions upon it. This new approach allows for real-time, current information to be received and used. No longer will careful drivers have to pay extra for the less careful ones because the offers can be individualized for each and every customer. It not only provides more precise information but also saves money that would otherwise be spent on costly assessments and audits.
The application is not limited to car insurance. Not only can wearable devices track the client‚Äôs health parameters, but also how healthy his behavior is (for example,whether he exercises enough).
The insurance industry was among the first to start actively using facial recognition in various aspects of its work. Although this technology is far from achieving all its potential, the applications are already impressive and are going to get even more so in future. For example, insurance startup Lapetus already offers its clients an opportunity to buy life insurance using a selfie. It is built around running an analysis of facial patterns to discern signs of life-threatening habits such as smoking. As they serve as a strong predictor of lifespan, Lapetus decided to use them in lieu of expensive, uncomfortable and time-consuming medical examination.
The insurance industry already actively uses chatbots ‚ÄĒ they help build up the initial communication with the customer without having to resort to human employees whose efforts may be better applied elsewhere. This approach allows for moving the entire interaction between the company and the client online, dramatically decreasing operational costs and thus lowering the price of premiums. And any company that only works with its customers online has to rely on machine learning to prevent fraud and guarantee that every customer gets individualized experience.
The times when customers were offered a limited set of options and were asked to choose from among them are probably coming to an end. Modern business is all about customization and customer experience, and this trend has already touched the insurance industry as well. One example of such approach is Allianz1, a web interface that basically allows clients to build their own insurance policies from modules offered by the company.
Two of the most important factors defining the efficiency of an insurance business is how fast it manages to settle claims, and how successfully it does it. Introduction of AI dramatically boosts both of these factors.
A good example of a difference in speed is Lemonade‚Äôs AI Jim that made the industry news in January 2017 after settling a claim in less than three seconds. This is many orders of magnitude faster than the best human specialist can ever hope to achieve, as it rarely takes the less than a few weeks.
It is physically impossible for human insurers to gather and process all the information about policyholders that can be an indication of fraud. Companies that rely on AI solutions are capable of processing virtually unlimited amounts of such information, which means that claims are settled not just faster than it is done traditionally, but also with a much lower percentage of fraud. Additional use of machine learning for fraud detection also means that AI learns to improve their results over time, getting the ability to notice the telltale signs of fraud more efficiently as they encounter its new and new instances.
Needless to say, this is a major opportunity for saving money ‚ÄĒ insurance companies report more than $80 billion in fraudulent activities a year, and any technology that allows for their better and less effort-intensive detection is a godsend.
As you can see, the introduction of AI into the insurance industry is a textbook example of digital disruption. It already changes the industry, and an absolute majority of insurers agree that in a few years‚Äô time it is going to be completely transformed. Insurance companies cannot afford to be conservative ‚ÄĒ the only way to stay ahead of the game is to embrace the change as early and as bravely as possible. If you do it early on, you will be able to meet the change being completely ready to reap the best advantages of the changing business landscape.
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