Scalable AI: Out of the lab and into your insurance business
A lot of businesses still find implementing AI to be a struggle. Even though many companies have already kicked off AI prototype or pilot projects, stabilising algorithms or otherwise supporting scalable AI remains a challenging solution. According to Gartner Research, only 15% of use cases through 2022 leveraging AI techniques such as machine learning, deep neural networks, and Internet of Things environments will be successful. Regardless, AI technology is developing at such a high speed that scalable solutions are beginning to emerge more frequently.
Overcoming black box systems
Applied AI and new AI-related business models
Scalable AI as a present-day strategy for the future of insuring
As new ways to construct algorithms continue to take shape and generative modelling evolves, scalability will in turn become more attainable and evaluation metrics will thus improve. This will bring exciting changes to insurtech models in risk management, underwriting, insurance pricing as well as customer relationship management. We can also expect to see traditional business continue to evolve too as more data points are used, providing more in-depth UW, the automation of internal processes such as claims, the automation of external processes, e.g. interfaces to doctors and progressive support for product generation.
Munich Re regards the broader sector of AI as one of its strategically important topics – one that will create new competitors as well as completely new emerging business opportunities. The feasibility is already evident across all industries and, in particular, insurance companies. Munich Re is dedicated to the belief that AI should serve people while all ethical aspects are respected, and fairness and justice are ensured. Within this principled sphere, we will continue to research AI, while helping our clients maximize the potential of AI for their business.