CIAA Southwestern Ontario Fall Seminar Wednesday, November 20, 2019 | 2:00 PM – 4:30 PM
VENUE Langdon Hall Ground Floor, Firshade Room 1 Langdon Drive, Cambridge, ON N3H 4R8 519-740-2100
TICKETS Member Rate -$125* Non-Member Rate – $175* *All tickets are taxable at 13%
Please join us for the CIAA Southwestern Ontario Fall Seminar where we will have two engaging topics up for discussion, followed by a networking and social time. The Fall Seminar will be held on November 20th, 2019 at 2:00 PM at Langdon Hall in Cambridge, ON.
PROGRAM INCLUDES What do Innovation Labs and Insurtechs in our region mean for Southwestern Ontario Insurers: If insurance companies are to thrive and be competitive, insurers need to leverage and integrate Insurtech solutions, either through acquisitions, direct investments, innovation labs or services agreements. We will be posing key questions to a panel of local experts:
What is an Innovation Lab and how does it differ from an Insurtech?
What do they do inside Innovation Labs?
What is the value proposition for investing in an Innovation Lab or an Insurtech?
How can local insurance companies benefit from the innovation in our region?
How insurers can share data without putting their customers privacy at risk. The value of data is often greater than the sum of its parts. In the financial services sector specifically, the use of data allows financial institutions to offer greater value and personalized services to clients and address business challenges such as fraud. However, the use of data raises privacy and security concerns from customers, institutions, and regulators – these competing obligations have historically prevented institutions from unlocking the full value of their data. Now, emerging privacy enhancing techniques (PETs) have the potential to fundamentally alter these dynamics by reducing or eliminating the privacy risks of sharing data and opening the opportunities to create value. This session will discusses five PETs that allow institutions, customers, and regulators to analyze and share insights from data without distributing the underlying data itself.