Generative AI is currently revolutionizing many industries and also has the potential to sustainably change insurance companies. The possible uses are diverse and range from sales to underwriting to customer service. Along the entire value chain, GenAI can accelerate processes, improve decisions and make experiences more personal.
Pegasystems gives five examples of the use of GenAI by insurance companies that show how versatile the technology is and the opportunities it offers:
1. Better customer service
Chatbots, voice bots and virtual assistants based on generative AI can not only take care of customer concerns around the clock, but also in different languages. The bots and assistants, for example, answer questions, record claims and help with product selection. This takes customer service to a whole new level and relieves the workload of employees in the service center.
Instead of spending a lot of time answering standard inquiries, they can fully concentrate on advising customers and processing more complex claims. In addition, GenAI also enables you to interact more personally with customers because the algorithms can listen in on conversations and automatically provide helpful information, such as customer history or similar claims in the past.
2. Tailored insurance products
With generative AI, customers can no longer just be classified based on demographic characteristics, but also based on their preferences and behavior. Based on this, individual risks can be derived, which allows very precise customer segmentation and precise targeting.
In this way, insurance companies receive clearly defined customer groups that reflect the diversity of society and can be targeted with tailored insurance products.
3. Real-time risk assessment
Until now, risks have been assessed at a certain point in time – usually before an offer is made for the potential insurance customer. However, with generative AI, insurance companies can analyze various parameters in real time and thereby make their tariffs much more flexible. This is already partly implemented in telematics tariffs that reward safe driving.
It would also be possible to evaluate the sensor data from smart home systems in order to adapt premiums to the specific risk, or to design accident insurance in such a way that premiums increase or decrease depending on whether and when high-risk sports are practiced. This would lead to fairer prices for insurance customers and make the corresponding tariffs more attractive.
4. Prevention as a new business model
By evaluating real-time data, insurance companies can continually update their risk assessments and respond promptly to increased risks. However, classic predictive AI quickly reaches its limits because it only recognizes patterns and anomalies that occur in its training data – it is overwhelmed by new and unforeseen events.
This is where generative AI comes into play, which can adapt better to new situations and could even proactively suggest or initiate risk-reducing measures when risks increase, such as replacing components if sensor data indicates an impending system defect. “Prevention” could thus become a new business model for insurance companies. They would not just step in in the event of damage, but would help prevent damage in advance, which would improve their profitability.
5. More efficient claims processing
With generative AI, damage reports can be analyzed automatically in order to capture all important information in a structured manner. GenAI can also evaluate images of damage in order to make comparisons with previous cases or to detect fraud.
It then provides human claims handlers with valuable information about the type and amount of damage, ensuring fast and efficient processing that benefits both the company and the customer. In the case of smaller amounts of damage, she could even decide independently. This would also help to avoid differences in damage assessment by different employees.
“Generative AI is a tool that insurance companies cannot do without if they want to improve their customer service, operations and decisions,” emphasizes Elischa Göttmann, Principal Solutions Consultant at Pegasystems. “The use cases mentioned already show the potential that lies dormant in GenAI, and in the coming months and years there will probably be many more use cases that no one is thinking about yet.”
