Artificial intelligence may be a buzzword in the insurance industry, but many within it believe the potential applications could be of huge benefit to providers
GetSafe CTO Marius Blaesing (left) with co-founder Christian Wiens (right)
Artificial intelligence is a common buzzword in the insurance industry right now, as companies are quickly finding out how transformational it can be for their business. According to German insurtech start-up GetSafe, there’s also a community benefit to be had. Co-founder and CTO Marius Blaesing explains the state of play among insurers right now.
Google, Amazon and Facebook have led the way: data is now the currency of the future.
This is particularly true for insurers, who are aware that if you know your customers’ behaviors and life situations, you can price risks more accurately, minimise fraud and better meet their expectations.
The potential is enormous, especially for the insurance industry, where processes are data-intensive and often repetitive.
Many customer enquiries, claim reports or data analyses could theoretically be standardised and automated – ideal prerequisites for using intelligent machines.
Nevertheless, the insurance industry is one of the few industries to have barely arrived in the 21st century.
Paper-based processes and outdated IT systems with incompatible interfaces are the rule – not the exception.
For this reason, according to a study by strategy consultancy Bain, companies have so far concentrated in particular on using smart algorithms to make individual sales processes more efficient, or to provide more targeted information.
According to Bain analysts, however, the greatest potential slumbers in downstream areas, where property and casualty insurers alone could increase their premium income by almost 25% and reduce costs by almost 30% through digitalisation.
The greatest savings are therefore possible in claims settlement and acquisition costs.
Few companies in the insurance industry use complex artificial intelligence due to outdated infrastructure
As opinions on what AI actually is differ widely, let’s take a step back.
20 years ago, IBM’s Deep Blue chess computer defeated the then reigning world chess champion Garri Kasparow, with the event celebrated as a historic breakthrough.
Deep Blue did not win through cognitive intelligence, however, but because it could calculate all conceivable moves on the board.
Artificial intelligence in the narrower sense means more than just computing capacity.
It is based on algorithms that use large amounts of data to learn to carry out tasks without pre-defined rules.
Self-learning systems that make their own decisions, on the other hand, have so far largely been unsuccessful in the insurance world.
The reason for this is very few insurers have an IT infrastructure that would allow customer data to be bundled over the entire contract term and across their interfaces.
They therefore do not have sufficient high-quality and correctly classified training data with which to test a machine learning algorithm – relying instead on rule-based systems.
For example, some providers work with chatbots to process customer enquiries faster and with less input from human staff.
Digital language assistants – which process natural language and interact directly with the customer – point in a similar direction.
Whether in writing or by telephone, the basic principle is the same – employees are relieved of routine tasks so that they have more time for those questions where personal contact is really necessary.
Artificial intelligence will revolutionise the customer experience in the insurance industry
Experts agree that AI will revolutionise the insurance industry in the medium to long term.
Smart algorithms will help identify insurance fraud faster and assess risks more accurately.
This allows AI to be used to create personalised products, in combination with sensors and the “Internet of Things” (IoT), as well as helping to prevent fraud.
Together, these many possibilities will lead to insurance products that are more individualised and fairer for the collective.
If insurers can refine the risk profiles of their customers on the basis of their history and behaviour, they can determine whether or not each is trustworthy.
The subjective decisions of individual experts would then be balanced by objective, trained systems, that could not be influenced by emotions or stress.
Today, on the other hand, customers quickly end up in a box based on their occupation or postcode, which doesn’t truly represent their risk profile.
Artificial intelligence could evaluate trustworthiness on the basis of data and synthesise it for those in the insurance industry.
Customers who are trustworthy can then benefit, for example, from lower prices or faster claims processing.