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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 do you operationalize integrated budget planning to ensure business and your analytics/AI solutions functions stay integrated?

“Firstly, we are at the table together. We mutually agree that we need to grow the business and serve our customers. From there, our data and analytics teams assess whether we use AI for the solution or if we use something else. The challenge we are solving for isn’t how to find value in AI or how we can build the most sophisticated models; it’s how we grow the business and find value for our customers, employees, and shareholders. This is where our shared goals become so critically important. We’re not a hammer running around looking for a nail. AI is simply another valuable tool in our toolkit for us to help the business achieve their goals” (Verbatim: Dr. Yannick Lallement).

How are you ensuring ongoing communication and change management practices are being applied to support your functional excellence?

We have processes in place to manage our capacity, which means we are careful in the projects we select. The key here is the business having a good understanding of what we can and cannot do. Data & Analytics awareness initiatives are one way we aim to grow understanding and knowledge across the Bank. For example, we hold an internal data and analytics week annually and have over 1000 attendants from all corners of the organization learning about data, analytics, and technology and what it can do and has done for the Bank. We also have regular communications and presentations to help the business keep learning about how the use of analytics may further enable their business” (Verbatim: Grace Lee).

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What is one of your AI projects that you are most proud of?

During the pandemic, we developed a model to identify customers who may be at risk of experiencing financial distress, or the Customer Vulnerability Index. Through this exercise, we were able to identify around 2 million customers who might need our support. The entire Bank came together and created a response team to make calls and support our customers through proactive outreach. We advised our customers of special government assistance programs, as well as ways to reorient their portfolios so they would be better positioned to weather the storm. Not only did this help our customers in their time of need, we were also able to lower delinquency rates and reduce risk for the Bank. Additionally, through this period, we experienced a five-point increase in customer Net Promoter Score (NPS), which reflected our customers’ appreciation of our care.

We are so proud of this initiative as we were able to use AI in the pandemic to better serve all of our customers. It was an unprecedented time where there was a huge amount of uncertainty for millions of our customers at once. Serving all of them in a personalized way could only be accomplished through AI, digital enablement, and our people all working together. (Verbatim: Grace Lee).

How is AI Ethics being integrated into all your AI/ML programs?

We created a dedicated function devoted to Data Ethics, which reports directly to our Chief Data Officer. This team, in partnership with our privacy and risk functions, has developed an Ethics Assistant to support our purpose of making it easy to do the right thing, including for AI model builds. It is a comprehensive checklist to ensure our teams continue to focus on data ethics during model creation or co-creation with the business. And it’s really like in an airplane, you know, you have a takeoff checklist, you review everything to make sure that your plane is ready for takeoff. In this case, the Ethics Assistant helps us review that.

Using the checklist, we may either need to go back as modellers and change elements of the model that may create bias, or we go to the business and discuss the implications. If there is bias, we always go back to the drawing board and bring the business along with us for the journey. This way, they start to understand the responsible use of data in ways that are much more practical.

The checklist is also an important way that we educate people on those things they need to consider to ensure our modelling activities are aligned with our core values. There is constant turnover in our field, with new people joining the team every day. Tools like the Ethics Assistant help to bolster their training and up-skilling and give us confidence that we are using a consistent approach across the Bank (Verbatim: Dr. Yannick Lallement).

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