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Scotiabank’s AI and ML Solutions Center Has Digital Literacy and Talent Front and Center

This is Grace Lee, Vice President of Data and Analytics at Scotiabank, and Dr. This is a five-part blog series taken from a recent interview with Yannick Lallement.

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 deposit and market capitalization.

With more than 90,000 employees worldwide and nearly $1.3 trillion in assets, Scotiabank has invested heavily in AI, Analytics and Data, harmonizing an integrated function well supported by all lines of business. While their journey has been impacted by zigzagging along the way, the organization now has a strong foundation for bringing consistent value and impact to the business.

This five-part blog series answers these five questions:

Blog One: How is the advanced analytics functionality structured and what have been some of the most important operational challenges on your journey?

Blog Two: What does it take to set up an AI/ML Analysis Competency Center?

Third Blog: How do some operational challenges, such as Digital Literacy, affect your journey?

Fourth Blog: What are some of the operationalization lessons learned?

Fifth Blog: What awaits Scotiabank’s Advanced Analytics and Artificial Intelligence functionality in the future?

What challenges are you still facing? Is digital literacy one of them?

“Yes, digital literacy is still a great opportunity for us. Every time we start a new project, we initiate an exploration phase where we get data and analytics practitioners to talk to the business and work together to determine what the model will be and specifically how. It is used to determine the expected benefits and value, so that everyone is in harmony. The term we use to describe this process is co-creation. This process has two implications: one effect is that we get support from business leaders who will eventually use the model; and the second effect is that the designed model will fit their needs as they have been on the table since day one – so there is more cohesion, consensus and support for desired results.

Another challenge we face is getting the business to understand that artificial intelligence is more complex than regular software development. We are at a stage where we can benefit from more standardization of how we do things and more industrialization of how we do things. It becomes important to find ways to keep us agile while having a more formal process, better ways to share, reuse knowledge and even reuse models.” (Verbatim: Dr. Yannick Lallement).

How do you find talent and has it been difficult?

“It’s getting harder and harder to find talent. Demand far exceeds supply and will continue to be so. We’ve been very fortunate to have other markets where we can recruit, as our advanced analytics skills are becoming increasingly difficult to source in North America. We also recruit heavily internally – many of our analytics specialists are trained in business or functional areas within the Bank, so they have the depth of knowledge to add swift and practical value to our internal solutions teams. It is largely part of our culture that our technical practitioners understand the business and work in partnership across the organization” (Verbatim: Grace Lee).

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