7 take-aways from the Insurance Analytics Conference


I recently attended the Insurance Analytics Summit in Toronto. It was an interesting event, even if it was heavily skewed towards insurers. Here's what I learned.


  1. The "data revolution" has begun. Insurance is anchored in data and decision-making. The big difference now is that previous "revolutions" in insurance were focused entirely on business processes (Eugene Wen from Manulife talked about this). Today, exponential gains will come from data-driven thinking and applications.
  2. Structured versus unstructured data. Structured data (like what we capture in Policy Works) is the low-hanging fruit to moving on analytics and AI initiatives. But unstructured data, like information captured in photos, texts, and voice recordings, offer a rich and deep opportunity to extract insight. Tools to analyze unstructured data are in in proof-of-concept stage and are close to being commercialized.
  3. The convergence of 3 different economies. Connected economy. Data economy. API economy. The emergence of these 3 different economies, and their converging paths, is what really provides the framework for analytics and AI to explode.
  4. Sensors are everywhere. Think of mobile devices, FitBit or Apple watches, telematics and Nest or Alexa. All collect data that is being used to build deeper understanding of customers, of people. One interesting application was a water sensor (hardware) connected to an App that notified the customer of a potential water damage situation (this was demoed by Softelligence I believe).
  5. Focus on high-volume, routine processes. Find these workflows or interactions to start generating quick wins with analytics and AI. Look for quick wins.
  6. Use analytics and AI to identify missing values in your data. Almost all data sets will be incomplete. Yes, there is a cleansing to be continually done. However, AI might be able to help solve part of this.
  7. Data governance is key. Questions like, how is data captured and stored? Who has access to the data? Is it secure? In the rush to capture, analyze and commercialize (or monetize) data, data governance is key for any organization.

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Topics: Commercial_lines, data, analytics