Scotiabank is leading the way with advanced analytics and artificial intelligence

This is a five part blog series. from an interview I recently had with Grace Lee, chief data and analytics officer, and Dr. Yannick Lallement, vice president of AI and machine learning solutions at Scotiabank.

Scotiabank is a Canadian multinational banking and financial services company headquartered in Toronto, Ontario. One of the Big Five Banks in Canada, it is the third largest Canadian bank by deposits and market capitalization. With more than 90,000 employees worldwide and assets of approximately $1.3 trillion, Scotiabank has invested heavily in AI, analytics and data, and has aligned an integrated function that is supported by all lines of business. Although their journey has had a zigzag impact along the way, the organization now has a solid foothold to deliver consistent value and impact to the business. We can all learn a lot from these words of wisdom about what it takes to successfully advance AI in a large company.

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 biggest operational challenges in your journey?

blog two: What does it take to set up an AI/ML solutions competency center?

Blog Three: How are some of the operational challenges like digital literacy affecting your journey?

Blog Four: What are some of the operational lessons learned?

Blog Five: What does the future hold for Scotiabank’s AI and advanced analytics capabilities?

How is your organization structured in terms of analytics, data, and AI?

“If you’ve been following our recent history, you know we’ve had a lot of ups and downs. We have had some failed attempts to bring analytics and data to the Bank in a meaningful way. And through this journey, we have learned from our mistakes to enable us to move from isolated AI, data and analytics professionals to a unified center of excellence where we have integrated teams across various lines of business and functions. Previously, we had data in a primarily governance function in our risk management function where they focused primarily on data quality, but didn’t do much data enablement or delivery.

We currently have more than 500 analytics, data, and AI professionals, and about half are AI experts. We have a fairly diverse team in terms of skills, ranging from business analysts, user-centric designers, data scientists, data engineers, NLP specialists, ModelOps engineers, as well as resource experts in data and AI ethics. Our people are mainly in North America (75%) and the rest of our talent is located in different global regions, in Mexico, South America (Peru, Chile, Colombia), the Caribbean, etc.

We are proud that our team is made up of people who can ensure that our AI and ML modeling solutions are effectively designed and implemented from inception to consumption.” (Verbatim: Grace Lee).

What were some of the most significant lessons learned on your organizational restructuring journey?

“Just because we have AI, analytics, and data as capabilities doesn’t mean we’re creating value, and if we’re not creating value, we don’t have a place in the Bank. So one of the things that we said we need to do differently is instead of putting the function in technology or operations or marketing where these teams often live, we’ll have data reporting and analytics directly to the business lines. We had to make sure that the value came from the business users using the solutions and creating tangible value.” (Verbatim: Grace Lee).

What were some of the technical lessons learned?

We learned that by closely marrying data and analytics, aligned with technology, and having shared priorities and goals set by the business, it’s less about the sophistication of the model and more about the meaning of the outcome.

We have learned that we must work closely together in this ecosystem that we have built. This allows us to activate the virtuous cycle between data, analysis and technology because technology is necessary to make data; data is needed to make models; and models must be reintegrated into technology to face a customer and employee by being integrated into the operational process. If we do not ensure the integration of processes, we are not working in harmony.

For example, if we build an AI model where data pipelines are built all at once and are not sustainable, when something changes in the technology, the models will no longer work properly and support the business; this scenario is the antithesis of the way we think about delivery value. When we talk about bringing data and analytics together, it’s not just about data governance, it’s about data delivery. Our concept of a set of reusable authoritative data underpinning models to ensure operational sustainability is considered from the outset and is central to our strategy.

This allows us to provide an abstraction layer that allows end-user data to remain consistent and persistent, so that if something changes in legacy systems, we can still deliver that same high-quality data to all of our models. This means that our reporting and processes are somewhat insulated from technological change. In other words, as you know in AI, often 80% of the problem is in the data supply; with accessible and well-managed data, we expect it to be closer to 20%.” (Verbatim: Dr. Yannick Lallement)

Note: See Blog Two: What does it take to set up an AI/ML solutions competency center?

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