Scotiabank’s AI And ML Solution Center Has Digital Literacy And Talent Front And Center
This is a five-part blog series from an interview that I recently had with Grace Lee, Chief Data and Analytics Officer and Dr. Yannick Lallement, Vice President, AI & ML Solutions at Scotiabank.
Scotiabank is a Canadian multinational banking and financial services company headquartered in Toronto, Ontario. One of Canada’s Big Five banks, it is the third largest Canadian bank by deposits and market capitalization.
With over 90,000 employees globally, and assets of approximately $1.3 trillion Scotiabank has invested heavily in AI, Analytics and Data and aligned an integrated function that is well supported by all business lines. Although their journey has zig zagged in impact along its way, the organization now has a strong foothold in bringing consistent value and impact to the business.
This five-part blog series answers these five questions:
Blog One: How is the advanced analytics function structured and what have been some of the most significant operational challenges in your journey?
Blog Two: What does it take to set up an AI/ML Solutioning Competency Center?
Blog Three: How are some of the operational challenges like Digital Literacy impacting your journey?
Blog Four: What are some of the operationalization lessons learned?
Blog Five: What does the future hold for Scotiabank’s Advanced Analytics and AI function?
What are the challenges that you’re still facing? Is digital literacy one of them?
“Yes, digital literacy is still a major opportunity for us. Every time, we start a new project, we kick off a discovery phase where we ensure that the data and analytics practitioners speak with the business and, together, we work to nail down what the model is going to be and specifically how it’s going to be used, and to identify the anticipated benefits and value, so everyone is aligned. The term we use to describe this process is co-creation. This process has two impacts: one impact is we get buy-in from the business leaders who will eventually use the model; and the second impact is that the model being designed will fit their needs because they have been at the table from day one – so there is more alignment, consensus, and support for the desired outcomes.
Another challenge we face is ensuring that the business understands that AI is more complicated than regular software development. We are at the stage where we can benefit from more standardization of how we do things, and more industrialization of how we do it. Finding ways to ensure we are being agile, while having a more formal process, better ways of sharing, reusing the knowledge, and even reusing the models becomes important” (Verbatim: Dr. Yannick Lallement).
How are you finding talent and has it been difficult?
“Talent is increasingly difficult to find. Demand is far outstripping supply and will continue to do so. We’re very fortunate to have other markets in which to recruit, as sourcing in North America is increasingly difficult for advanced analytics skills. We also recruit heavily internally – many of our analytics professionals grew up in business or functional areas within the Bank so they have the depth of knowledge to bring rapid and practical value to our internal solutioning teams. It’s very much part of our culture that our technical practitioners understand the business and work in partnership across the organization “(Verbatim: Grace Lee).