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Machine Learning Innovation Challenge - What Can Be Accomplished in 24 Hours?

[fa icon="calendar"] Sep 21, 2017 3:49:52 PM / by Jason Yu, Innovation Chef

Jason Yu, Innovation Chef

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What can you accomplish in 24 hours when you bring together a group of curious thinkers?

Cookhouse Lab hosted our first free Innovation Challenge Event last week and the topic of interest was machine learning. Machine learning has been a definite hot topic for manual process optimization in any industry, and our participants were taken through a condensed Cookhouse Lab innovation sprint towards creating insurance specific use cases. 

Video Recap

 


Inspired by the recent Delays in APS project,  we invited the insurance community to embark on a machine learning minimum viable product by piloting a 24 hour innovation challenge. Participants came from all facets of insurance including The Cooperators, Munich Re & RSA. Other diversifying participants included KPMG and Insurance Canada, both of which added greatly to the experience.

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After orientation with our in house Cookhouse Lab design thinking team and machine learning experts SortSpoke, the teams enjoyed some networking over a BBQ dinner and a team formation exercise in our rooftop patio.


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The group shared many fantastic ideas that spanned automotive, oil & gas and social industries to life and P&C insurance products, but the group narrowed down their choices to the top three.

Teams were formed around three different machine learning themes: Chatbots, Fraud and Social Media.

A facilitated rapid design thinking experience was delivered over the reaming competition timeline. This included defining a problem statement, identifying target personas, designing a solution and most importantly, testing the design with SMEs and customers. Though the team didn't have the time to generate multiple iterations, the final MPVs presented to judging panel consisted of Chris Murumets, Co-Founder and CEO at LOGiQ3 Group and Cookhouse Lab, Hashmat Rohian, Senior Director, Innovation at The Co-operators and Craig Mauchan, Vice President Sales & Marketing at SortSpoke, were top notch quality. 


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Through this Innovation Challenge, I found that the following three key factors enabled success:

  • Preparing a testing and SME community is key to rapid design thinking experiences, and design thinking is something that can be adapted to any phase of the project.

  • Whether it may be three months or three hours, keeping a customer centric and lean startup mindset will consistently provide insightful and quality output with any diverse team.

  • In contrast to traditional work, the ability to instill a close but not perfect attitude helps teams progress when given constrained timeframes. By not focusing on perfection or knowns, teams can continue to experience, experiment, trail and error towards an MVP goal without expending energy validating outputs. Remember there is always another chance at the next iteration!

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(Sample output)


In the end, participants, judges and the Cookhouse Lab team all enjoyed the high energy experience.  Many were amazed at the output and the learnings from the 24-hour experience. The judging criteria was based on four factors: MVP Presentaton (40%), Innovativeness (25%), Size of Opportunity (20%) and Time to Market (15%).  And in the end, the winning team edged out a win by scoring only 0.025 more points than the second place team but, everyone deserved to win!

Do you have what it takes to innovate under time constraints? Does working with a group of diverse peers excite you? 

Be sure to subscribe to get the latest announcements on our next 24-hour challenge and release of our next innovation projects! 

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Topics: innovation challenge, machine learning

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