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?

How have you created an internal AI / analytics consulting function to support meeting the ever-changing business needs?

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In 2021, we created a new executive position within the bank that is dedicated to what we very purposefully called AI and Machine Learning Solutions. This is run by Dr. Yannick Lallement, and this solution group is responsible for incubating ideas, creating new AI products, and integrating AI and machine learning at scale into business operating processes.

Having our practitioners at the table alongside business leaders is incredibly important. We bring AI/ML and technical expertise alongside the business, ideating and building solutions together. But Scaling requires controls. Otherwise, we would rightly would run afoul of our regulator and our own principles around data ethics pretty quickly. To solve this, we must have a close working partnership and balanced relationship between the business, technology, data, and analytics leaders.

There is often natural contention between analytics talent who often want to focus on just building great models, and business or risk functions who need an element of control of the process and the data. By not partnering, neither succeed in their goals and the Bank and its customers suffer. Having leadership focused on building those partnerships and truly collaborating has made all the difference in our ability to move forward together (Verbatim: Grace Lee).

What percentage of your AI models actually make it into sustaining production?

“We have a relentless focus on last-mile execution and tangible benefits, so over 70% of our work programs are in production. The remaining 20-30% are still in process, but the ultimate goal is to bring all the models into production – which is why strong selection criteria and business ownership are always front and center” Verbatim: Dr. Yannick Lallement).

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