Insurers face legacy system roadblocks for AI implementation

Published: March 8, 2019

Updated: April 1, 2019

Author: Luke Jones



Earlier this week, we reported on how artificial intelligence (AI) will be able to save the insurance industry billions of dollars each year. However, Canadian insurance companies face a roadblock in their AI expansion caused by legacy systems. Often viewed as a major area holding back the digital transformation of Canadian insurers, said a speaker at last week’s Technology Conference.

“There’s a bunch of legacy software out there, so we have to interact with legacy systems and legacy databases and they don’t necessarily have modern-day AI capabilities,” said Stephen Piron, co-founder and co-CEO of Dessa, a North American company specializing in AI deployment.

Other barriers also exist, such as a lack of AI expertise within insurance companies. Employees within organizations are not trained in cutting-edge AI implementation.

“Finally, once you have a model in deployment and it needs to be retrained, there’s a bunch of aftercare that needs to happen and that aftercare becomes much more complicated the more models you put in place.”

Creating AI infrastructure at scale is a particular challenge that comes with many obstacles. However, during a session titled Getting AI Right the First Time, Piron discussed the many benefits of implementing AI within a business:

  • Get the “right” customer
  • “Supercharge” advisors and sales agents
  • Create more accurate price risk models
  • Make the claims delivery process faster and more efficient
  • Reduce insurance fraud

Piron references Scotiabank, which used artificial intelligence to model credit card behaviour. While “traditional statistics” were used to create the behaviour model, Scotiabank was forced to use a new model each time new credit cards were issued or when geopolitical changes. This human-created model took six months to build.

“The outcome of this was that it took a tremendous amount of work to put this through because we needed to build all of the infrastructure necessary to do cutting-edge AI in the enterprise system before we could even do it,” Piron said. “It necessitated us building our own tools and really underlying the need for all our tools. Thankfully, it worked out and created a bunch of momentum in this organization, which lay the groundwork for us to do further things.”

Yes, it is an expensive and time-consuming project to tear apart old infrastructure and pursue investment in AI. Piron says “companies that build the foundational structure for AI now will have exponential competitive advantage over those who don’t.