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“The underestimated optimization potential of the insurance industry” | Fraunhofer IAIS
Since the beginning of the year, the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS has been a member of the InsurLab Germany community. Dr. Rafet Sifa is head of the business unit “Cognitive Business Optimization”, deputy head of the Media Engineering department and Lead Data Scientist at Fraunhofer IAIS. In this interview, he talks about what he thinks is the underestimated potential of artificial intelligence (AI) for the insurance industry.
Today we're talking to Dr. Rafet Sifa about underestimated use cases of artificial intelligence in the insurance industry. Why is this topic so important to you?
One of the main reasons for using AI in the insurance industry is to optimize customer experience. There are already many innovative solutions concerning this topic, such as chatbots or self-service portals. Unfortunately, I have heard that chatbots in particular have not generated the response from customers that insurers had hoped for.
My experience is that there is a certain skepticism and dissatisfaction with AI-based solutions. Some people just straight up "don't believe" in AI as a solution. This is also reflected in the numbers: According to a 2020 study by Capgemini, only a minority of companies are currently using AI solutions. Just 5 percent of banks and 6 percent of insurers engage with their customers via AI-based applications. Smaller proof-of-concepts are being deployed, but comparatively few AI applications are actually going live. Our customers tell us that the biggest hurdles are data protection and regulatory issues in particular – keyword "trusted AI" - and the costs associated with the implementation of AI-based solutions.
The current situation is that most German insurance companies have underperformed in comparison to the international competition in recent years. As far as digitization and innovation are concerned, Germany is certainly not leading the charge. Data-driven business models are the future. To stay relevant, insurers must utilize the insights gained from evaluating Big Data and realize cross-functional optimization potentials. Despite the bad first impressions of AI, insurers should continue to invest, cultivate talents, and build the necessary infrastructure. It is now important for German companies to stay on the ball.
So how do you suggest making use of AI-based solutions?
Digitization is nothing new for the insurance industry. As I mentioned before, there are many innovative solutions to make customer experience more efficient and better. Nowadays, insurers are engaging with customers in a more modern way. Sure, there is still room for improvement here as well. But I think that in the process of modernizing the frontend, many processes and outdated systems have been neglected and those definitely need an overhaul.
For example, there are a lot of documents that are already processed automatically. In Germany, we call it “dark processing” because usually those processes happen without the notice of the customer in the back office. Unfortunately, the rule sets used for this are often so complex and bloated that adjustments for further optimization are too expensive and take too long. As a result, dark processing automation lags behind the already modernized front end. I think that this problem could be solved by using AI, as it uses examples to learn and understand the way documents are processed. Changing individual rules is not a problem, as the AI has understood the systematic behind the processing and continues to function without costly readjustments. There is still a lot of potential here for the use of AI to greatly reduce administrative costs.
You are head of the business unit “Cognitive Business Optimization”. What distinguishes your business unit from the others? What exactly are you doing?
I just mentioned the often-underestimated optimization potential in the insurance industry. This is exactly what we specialize in. The “Cognitive Business Optimization” unit focuses on the optimization and automation of processes. With state-of-the-art methods of artificial intelligence, we can automatically extract all relevant information, for example from contracts or business reports, and review it for their consistency and plausibility. By individually adapting which information and KPIs are filtered, we make sure to always custom tailor our solutions to the client’s needs.
That sounds very promising. How are your AI-based solutions already being used?
It's really promising and incredibly exciting to study and shape the current technological trends. To make the use cases of our AI-based solutions a bit more tangible, I like to tell you about a project for one of our latest customers. We developed a tool for the auditor PricewaterhouseCoopers GmbH to check the consistency and completeness of annual financial statement notes and financial reports. To do this, our AI had to be able to answer a few key questions:
- How does an auditor even judge whether a document is complete or not?
- Which KPIs and text passages are relevant to answer the questions at hand?
- Where do we find relevant text passages and KPIs in the documents? In one place or scattered across multiple sections and different documents?
- Do we need to put the information we extracted into context of other text passages?
- How do we identify outliers or anomalies?
During development, we incorporated the expertise of auditors so that the decision-making process of an auditor can be mapped and replicated to a certain degree. Our tool analyzes every single transaction, user, sum, and account to detect anomalies. As a result, the audit process became much more efficient and secure. You can certainly imagine that some of the questions our AI had to answer are not only relevant for auditors. There are comparable work steps across all industries.
There is a lot of potential for process optimization across the industry. However, there are many people who fear that their job could soon be replaced by an AI. What do you think about that?
Unfortunately, this is a very widespread opinion. Many people think of dystopian scenarios, such as those often depicted in Hollywood films. However, I can assure you that there will be no Skynet takeover in the foreseeable future. (laughs)
But for real now. Humans will still be needed in the future. A study by the McKinsey Global Institute predicts that around 5 percent of jobs as we know them today will not exist any more by 2030. Other sources claim that half of all the jobs that exist today will be gone. Predicting what is to come is already hard enough if you think about the rapid speed of technology evolving. I think that those predictions are not to be taken too seriously, the truth is most likely somewhere in between those estimates. What is however clear, is that there is a change, and it will come. Companies will and MUST utilize all the data they have access to for staying relevant. Some job fields and industries will be hit harder than others, that is sadly inevitable. Just don’t forget, we are the people in charge of implementing the AI in the first place, we can also decide to which degree we want to automize certain processes. We can also implement certain safety mechanisms. So, we should first ask ourselves whether an AI solution makes sense at all. Because that always depends on the case, including the quantity and quality of the data that we have access to train our algorithm with and how complex the problem we want to solve is.
"AI should not be seen as a threat, but as an opportunity."
AI will predominately be used to take monotonous and repetitive tasks off our shoulders, which can have a positive effect on the psychological well-being of employees. In this way, people will be able to devote themselves to more creative tasks. I believe this will do a lot of people a favor. Because when it comes to creativity, humans are irreplaceable. Furthermore, AI does not only take jobs away, but also creates new ones. For data-driven business models to work, we will need many people who are familiar with the administration of AI applications and the interpretation of data - so-called "Citizen Data Scientists"
And last but not least: What do you expect from the InsurLab Germany membership?
We want to convince insurers of AI with our innovative solutions and know-how. We want to drive the digitization of the industry forward. I am sure that there is unfathomable optimization potential in all areas across several industries where we can support with our AI solutions. However, for insurers to benefit from AI in the future, cross-functional optimization potential must also be identified. I hope that this blog post has provided food for thought and that some of you have become a little more aware of the different use cases that AI has.