Insurance is an industry built around risk. Insurance companies greatly depend on their ability to predict what risk a person, or organisation 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, machine learning and big data technology, means that insurance companies should be scrambling to adopt these technologies to quickly get the edge over the competition, but exactly do these innovations change the industry? Let’s look at some of the most important examples.
Faster Claims settlements (and when we say fast, we mean absurdly fast!)
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. The introduction of AI dramatically boosts both of these factors.
One of the best examples currently out there comes from American property and casualty insurance company, Lemonade. In 2016, Lemonade reported a claim-handling world record. The insurer reported a claim settlement speed of just three seconds and with no paperwork. According to the insurer, a policyholder submitted a theft claim for a $979 Canada Goose Langford Parka on 23th December 2016. Within 3 seconds, AI Jim, Lemonade’s artificial intelligence claims bot, reviewed the claim, cross-referenced it with the policy, ran 18 anti-fraud algorithms on it, approved it and sent wiring instructions to the bank, informing the policyholder the claim was paid at replacement cost and closed. Mind-blown!
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 customisation and customer experience, and this trend has already touched the insurance industry as well.
One example of such an approach is Allianz1, a new initiative from Allianz Italy. The platform essentially allows consumers to design and tailor policies based on “building blocks” from P&C, life and health insurance lines. In total, elements from thirteen insurance lines, including home, employment disability and telematics-enabled car insurance can be amalgamated into a single policy with the exact breakdown of coverage levels and premium allocations being displayed to the user. This user experience is accentuated by feedback about premiums in real time during the Alianz1 customisation process
Behavioural premium pricing
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 the driver goes on average, how quick they are to accelerate, how they brake, whether they are 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 — that is, companies study the client’s 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 individualised 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.
Decreased fraud opportunities
It is physically impossible for human insurers to gather and process all the information and unstructured data 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. The use of machine learning for fraud detection also means that AI learns to improve their results over time, getting the ability to notice the tell-tale signs of fraud more efficiently as they encounter its new and new instances.
Needless to say, this is a major opportunity for saving money — 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 demonstrated by the organisations above, the introduction of AI into the industry is a textbook example of digital disruption. Insurance companies cannot afford to be conservative — 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.
Posted by Helen Thomas