Marius Blaesing, founder and CTO at Getsafe, explores how some of the biggest players in insurance are jumping on the chat-bot bandwagon.
They´re known as Carlo and Allie (Allianz), novomind iAGENT (AXA) or simply Reiseassistent (ARAG): more and more insurers are using interactive chatbots to automate customer service. The talk is of neural networks, machine learning and digital language assistants. But how intelligent are the systems really? A closer look quickly reveals that most insurers are still miles away from artificial intelligence (AI). It fails because of a fundamental problem: Data.
It is so simple in theory -- you are shopping when your smartphone alerts you that the washing machine is leaking. A sensor on the machine detected that water was running out of it and immediately stopped the water supply. Identifying risks and preventing disasters instead of paying for damages - this approach would create a better business model for both customers and insurers.
But what is already technically possible in pilot projects fails in practice due to outdated IT systems. Artificial intelligence cannot be realized with programs from the last century, regardless of which buzzwords are used in the initial story.
Insurtechs want to close this gap. They have the technological knowledge, the lean structures, quick and agile decision making and often the necessary risk capital to implement innovative ideas quickly. The insurtechs do not lack the ideas or language: here, too, the founders throw around plenty of marketing jargon and superlatives. But what about AI?
Let's start with a definition of terms. The problem begins with the fact that everyone understands artificial intelligence to be something else. This makes it far more difficult to compare presumably smart solutions - in case of doubt, even taped announcements in holding loops are celebrated as sophisticated technical innovations.
Put simply, AI refers to computers that are capable of, more or less providing assistance, generally solving problems and making decisions on their own. AI is therefore an attempt to create systems that can provide services with human-like intelligence. But what does that mean? After all, there are highly contentious arguments about what makes a person intelligent or not. And even supposedly intelligent people cannot solve all decisions and problems. AI demands more than pure computing power and means by definition, that algorithms can learn from "experience".
The prerequisite for this is an infrastructure that allows customer data to be bundled over the entire contract term and all interfaces. Almost all providers are still battling with countless data silos. The marketing department knows how many customers read the newsletter, the sales department knows the number of customers from Berlin or Cologne, and the customer service department has a good idea about money paid and for which damages. These companies fail to link the individual data points linked with each other. As a result, each department works within its own IT system, with its own set of data and third party providers. In some cases, customers are bombarded with conflicting information from different departments across multiple communication channels.
At the same time, almost all insurance companies have heard the wake-up call of the "young savages" and are working intensively on digital solutions. The declared aim is to relieve employees of time-consuming routine tasks so that they have more time for more complex tasks. The potential for the use of AI is high: many processes are data-intensive and repetitive - good prerequisites for automating these processes.
We predict AI will fundamentally change four areas in particular: firstly, it will accelerate numerous insurance processes such as the conclusion of policies or the settlement of claims. This will make insurance products more flexible and customer-friendly. Secondly, AI will enable insurance fraud to be detected far quicker and risks to be assessed more accurately. As a result, insurance companies will be able to refine their customers' risk profiles and reduce their loss ratios. "Good", trustworthy customers could then benefit from lower premiums or repayments. Third, AI can be used to create personalized products. If the insurer can better assess the customer's needs based on historical or behavioural data, it is in a position to put together tailor-made insurance packages. And last but not least, AI, in combination with sensors and intelligent devices, helps with damage prevention. In manufacturing, machines can already be repaired before expensive downtime occurs in production. Similar concepts are also conceivable in the home, in the car or indeed healthcare.
However, AI is still in its infancy in many areas. The ERGO Group is experimenting with language assistants that can recognize and process natural language. The Versicherungskammer Bayern uses IBM's cognitive system; Watson is supposed to recognize anger or irony in customer correspondence. Basler Versicherung has automated the processing of glass damage according to its own specifications. Carlo", a chat bot that is to be offered via the Facebook messenger, is still in the test phase. The robot will calculate offers for car insurance in discussions with customers. And the Ergo subsidiary Europäische Reiseversicherung (ERV) and the Deutsche Familienversicherung (DFV) offer international travel insurance in purely digital form via Amazon's language assistant.
What these approaches have in common is that they are usually based on rule-based algorithms. In addition, according to a study by Bain, many insurers concentrate primarily on product development and sales when using AI. However, the greatest potential lies in downstream processes.
What most insurers lack to enable them to fully exploit this potential is data. Algorithms can only become intelligent if they are fed with data.
As one of the first insurtechs, Getsafe has now laid the foundation for machine learning: a unified insurance platform makes it possible to record and evaluate data across the entire customer value chain in a structured manner. Algorithms for automatic claims processing or fraud prevention are currently being trained. One, Lemonade, Coya and other insurtechs are working on similar solutions.
One thing is clear: the future of insurance is digital. Traditional insurance companies carry legacy systems with a large backlog of modernization projects. At least for now, this creates opportunities for young insurtechs to build competitive advantages. It remains to be seen whether they exploit these advantages for themselves.
