The insurance industry can utilise tech to work smarter, using AI algorithms to construct a truly fair insurance system, says Christian Wiens, Founder and CEO of Getsafe.
Intelligent systems like Amazon’s language service Alexa or Apple’s assistant Siri are changing our everyday lives. Artificial intelligence is also an issue in the insurance industry: in the future, smart algorithms will play a key role in exposing insurance fraudsters.
Google, Amazon and Facebook have shown the way: data is the currency of the future. This applies in particular to insurers. Because if you know your customers’ behavior and life situation, you can better assess their credibility, price risks more accurately and minimize fraud.
Nevertheless, the insurance industry is one of the few that has barely arrived in the 21st century. Paper-based processes and outdated IT systems with incompatible interfaces are the rule rather than the exception. For this reason, companies have so far concentrated primarily on making individual processes more efficient or providing better information.
Only about 27 percent of all insurers are considered digital masters, and it is above all the insurance industry that is predestined to profit from the use of artificial intelligence (AI). Large amounts of data, different data sources and a juxtaposition of different processes can be considerably facilitated by AI.
However, the greatest potential lies in the reduction of insurance fraud. Just two percent of customers cause 40 percent of the inefficiency in the system – according to the Gesamtverband Deutscher Versicherungen (Association of German Insurance Companies), and one in ten claims reports show inconsistencies. Every year, insurance companies pay around 4 to 5 billion euros for insurance fraud.
How can the system be made fair again? Putting all customers under general suspicion is not a solution – after all, 98 percent of all customers are honest and there are only a few black sheep.
For insurance technology startups, Insurtech for short, the answer is clear: computers have no problem recognizing patterns from millions of data and navigating in a 200-dimensional data room. People, on the other hand, do. In this respect, artificial intelligence can better assess the risk of fraud and recognize connections than a human being.
But how does artificial intelligence get to the bottom of insurance fraudsters? Are the machines really better than competent employees with decades of experience?
Such intelligent systems have been used in medicine for a long time. Here there are sufficiently classified and high-quality training data with which the machine can learn. This is exactly what AI is all about: an algorithm learns to carry out tasks with the help of large amounts of data, without having to predefine specific rules.
The expert systems in medicine are mostly rule-based systems that obey firmly configured rules. Nevertheless, these systems are already achieving spectacular successes in radiology or dermatology. In a study carried out at the National Centre for Tumour Diseases (NCT) in Heidelberg, researchers had both doctors and an algorithm evaluate 100 images according to whether they were a birthmark or a skin cancer. In the end, artificial intelligence was more precise than traditional clinical diagnostics.
Self-learning systems that more or less assist in making decisions will also change the insurance world. They are still largely a bad thing, because very few insurers have an IT infrastructure that would allow customer data to be bundled over the entire contract term and all interfaces. They therefore only have data within individual silos and not along the entire value chain.
Nevertheless, experts agree that AI will revolutionize the insurance industry in the medium to long term. While today’s customers tend to be categorised based on their profession or place of residence, that does not correspond to their actual risk profile, and artificial intelligence could change that. It could calculate trust based on data.
Customers who are trustworthy can then benefit, for example, from lower prices or faster claims processing. The subjective decisions of individual experts would then be opposed by objective, trained systems that could not be impressed by emotions or stress. In short: the insurance process becomes objective again – and the entire community of honest insurance customers benefits from it.