New technologies, and more particularly artificial intelligence, are full of promises. They are often seen as opportunities to fulfill all of our desires and projects.
But what can artificial intelligence (AI) really achieve in the insurance industry? What are the real challenges that it can tackle, the already existing solutions, and how to implement them?
In this article dedicated to the insurance industry, you will learn facts and real opportunities, but also the common pitfalls which should be avoided when starting AI projects.
There are many challenges that the insurance industry is facing and tries to tackle. Amongst others: the significant amount of administrative work, the rise of the digital world, and knowledge management. (Source: “Insurance productivity 2030: Reimagining the insurer for the future”, McKinsey & Company, October 2020)
The first two are significant as they are directly impacting the quality of the customer experience. Indeed, people expect their requests to be processed fast and are uncompromising on rapidity. Providing quick and quality answers is a real struggle when the quantity of claims is also significant.
If an insurance company is willing to enhance, or at least maintain its customer service quality, it should make sure those issues are fixed. (Source: “Consumer insurance survey 2020”, Accenture)
Concerning knowledge management, the transfer or the sharing of information is often hard to implement. A lack of consistency could also be a threat when tasks are likely to be subjective.
Solving those issues are also priorities if an insurance company wants to ensure its sustainability.
Here is the answer to our question. If those tasks were automated, then those concerns would be considerably reduced. This is possible thanks to AI, which explains the interest of insurers towards this technology.
After all, if all tasks that no longer require human decision were automated, people could focus on more complex and value-added tasks.
Because of the largely over-marketed AI technology, described as being able to solve any problem, people have come to accept that, indeed, anything can be solved by AI.
To make sure that an AI project will successfully deliver value, it is important to understand what it can do and how to make it a success.
There is also a common misunderstanding between automation with AI and automation with RPA solutions. When applying RPA, it is necessary to specify the rules of your processes. This is not the case with AI: the machine learns by itself. While this is a significant advantage, this is also the point where people can be disappointed. Indeed, it means that not all tasks can be automated. Those who can are the ones that are repetitive, which allow the machine to acquire experience. Rather, RPA and AI solutions work hand in hand and complete each other to further automate processes.
As a consequence, a major advice to avoid disillusion when integrating AI is to be surrounded by people who are experts in the field of AI.
Whether it is rerouting an email, answering it, classifying documents, extracting specific information from these documents (invoices, contracts, receipts, etc.), or identify fraud, these tasks no longer require human-level recognition and can be automated.
For the insurance companies which have already implemented AI solutions, there are many business benefits. From cost reduction to knowledge capitalization, the positive impact increases the appeal for such technology.
