What is the Innovation Series?

We interview innovation leaders from around the world and share their insights on corporate innovation.

Innovation Leader: Jeffrey Bohn

The Swiss Re Group is one of the world’s leading providers of reinsurance, insurance and other forms of insurance-based risk transfer, working to make the world more resilient. Headquartered in Zurich, Switzerland, where it was founded in 1863, the Swiss Re Group operates through a network of around 80 offices globally. Jeffrey Bohn is responsible for driving R&D and innovation at the Swiss Re Institute (SRI), as well as overseeing the Swiss Re Institute focused on research supporting Swiss Re's clients, business units and group strategy.  

What has been your biggest learning as an innovation leader over the last 12 months? 

Innovation is as much about culture and organizational dynamics as it is about technology and business-model disruption. When I started my career as an academic 25 years ago, I thought innovation would naturally follow from new technology and "out-of-the-box" thinking. I was wrong to focus narrowly on tech and ideas. 

As we have transformed our innovation process in the last 12 to 18 months within our firm, I have come to realize that even the best ideas die when the culture and organization are not prepared. It is analogous to planting seeds in nutrient-deprived soil—the seed quality is irrelevant if it can't access the right environmental inputs.

Which technology are you most excited by and why? 

Confidential computing and privacy-preserving analytics: In the past few years, several threads of research in both the hardware space in the area of trusted execution environments (TEE) and the software space in the area of applying approaches to generate analytics from fully homomorphically encrypted (FHE) data constitute promising steps toward unlocking new value for many industries, in general, and financial services, in particular. Here is the problem: Much of the most valuable data for developing better models to forecast time series, manage risk, develop personalized solutions, and support better decision making cannot be processed due to data privacy considerations. 

With TEEs and/or privacy-preserving analytics, these (previously unusable) data will become inputs to new derived analytics in which no one in the analytics generation process (including the researchers and IT administrators) can access the raw, granular data. These technologies also add another layer of cyber safety. For example, data that are always encrypted (if one were to use approaches to derive analytics from FHE data) are much more secure given that even if the data are stolen from inside an enterprise's firewall, they would still be encrypted. (Note to be safe into the future the encryption will need to be post-quantum resistant i.e. uses encryption that can not be broken by a future, yet-to-be-developed quantum computer.)