As one of Canada's Big Five banks, the Bank of Nova Scotia is taking an approach to data, analytics, and AI intended to better understand and serve customers, said Grace Lee, its chief data and analytics officer. Her charter is to advance business growth, customer experience, and operational efficiency through the use of AI, machine learning, and data-driven insights at the bank better known as Scotiabank. The stakes in customer retention are high: Scotiabank has more than 10 million retail, small business, and commercial customers in Canada, as well as 10 million retail and commercial customers in Latin America, the Caribbean, and Central America. The bank has about 90,000 employees and assets of about $1.2 trillion. Over the past couple of years, Scotiabank has engaged in an AI strategy that is very focused on last-mile execution, Lee said.

That notion is belied by Scotiabank (officially the Bank of Nova Scotia), which has pursued a results-oriented approach to artificial intelligence over the past two years. While some of its resources are devoted to exploring how new technologies -- including blockchain and quantum computing -- might drive fresh business models and products, the great majority of its data and AI work is focused on improving operations today rather than incubating for the future. By all accounts, this integrated reporting structure is what allowed Scotiabank to move rapidly to gather and manage the necessary data and put analytics and AI capabilities in place. The analytics application uses a machine learning model -- called the Customer Vulnerability Index -- to identify consumers who are likely to have short-term cash-flow issues, using transactional data such as deposits and spending levels. It has found substantial returns from automating tasks in the back office of its global banking marketing division and improving security on the front line.

Some organizations may feel that acquiring AI capabilities is a race, and if a company starts late, it can never catch up. That notion is belied by Scotiabank (officially the Bank of Nova Scotia), which has pursued a results-oriented approach to artificial intelligence over the past two years. While some of its resources are devoted to exploring how new technologies -- including blockchain and quantum computing -- might drive fresh business models and products, the great majority of its data and AI work is focused on improving operations today rather than incubating for the future. As a result, Scotiabank -- one of the Big Five banks based in Canada -- has caught up to competitors in some crucial areas. It has done so by more closely integrating its data and analytics work; taking a pragmatic approach to AI; and focusing on reusable data sets, which help with both speed and return on investment.

Since 2016, Royal Bank of Canada (RBC) has focused on AI through its dedicated research division called Borealis AI. In October 2020, Borealis AI and RBC announced the launch of Aiden, an AI-driven electronic trading platform for institutional investors. Aiden uses "reinforcement learning," a form of AI based on behavioural psychology that either rewards or penalizes an algorithm when it makes a decision. This is the same type of machine learning used for AlphaGo, a Google-owned computer program that beat a human player at Go, a sophisticated board game, in 2016. But making trading decisions in a live market is more complicated than playing a board game, noted Foteini Agrafioti, chief science officer at RBC and head of Borealis AI. "It's a much more complex environment where we were able to deploy [Aiden] and we're extremely happy with the results," she said.

What does AI mean for businesses big and small? What key opportunities and challenges does it present? Two experts on the topic weigh in: Rotman School Dean Tiff Macklem and Scotiabank CTO Michael Zerbs. Can you talk a bit about how you're leveraging third-party datasets as part of your AI strategy? MZ: If you work for a large organization, never underestimate the challenge of just getting at the data that you think you've already got.

The fact that fraud is on the rise is not new, nor is it surprising that banks are turning to artificial intelligence (AI) and machine learning to fight back. Banks are, however, revamping their approaches to these technologies on how they may be applied outside of their typical use cases, fending off cybercriminals who have a growing number of opportunities to access online banking platforms and customer data. In the latest Digital Banking Tracker, PYMNTS looks at how banks are currently approaching their use of AI and machine learning in fraud protection and technology innovation. Competing in today's digital banking space is not as simple as opening a fully digital bank, as U.K. institution Barclays found. The bank has shuttered plans to open such a service in the U.S., stating that the project was proving too costly. Barclays will instead keep up its co-branded card efforts in the country at this time, but may revisit the project in the future.

On stage at a SAS Canada event hosted in Toronto, Creative Destruction Lab founder Ajay Agrawal invites the audience to engage in a simple "science-fictioning" exercise – imagine your business had a dial that could turn up the accuracy of your ability to predict things. At what number would turning up that knob suddenly change the entire way you do business. Agrawal points to a familiar product-recommending ecommerce giant to illustrate the effect this would have. "For Amazon, they don't have to get to Spinal Tap levels of accuracy, if they can get to six out of 10, then someone at Amazon says'why are we waiting for people to even order, why don't we just ship it?" he says. "Now you just return what you don't want and the competition is preempted that you might buy the products you do keep elsewhere."

TORONTO, January 25, 2017 - Today NextAI announced major partnerships with several leading Canadian companies to help fund and support the program and its participants. NextAI is a global innovation program for artificial intelligence-related ventures, and talented teams from around the world are invited to Canada to leverage the nation's leadership in AI and are provided with capital, mentorship, education and networking opportunities. The concept for NextAI was born out of an innovation brainstorming session sponsored by RBC and Magna during the summer of 2016, and joining RBC and Magna as founding corporate partners are BDC Capital and Scotiabank. To date, the funding provided by all corporate partners totals $5.15 million. NextAI has also formed technology partnerships with IBM Canada, Google and NVIDIA, who have committed millions in hardware, access to technology, and services.

Canada-based Scotiabank has announced a three-year sponsorship of AI firm NextAI, providing more than $1 million in funding to help build entrepreneurship in in the industry. The bank will also provide speakers, host events and support participants. NextAI, launched in 2016, aims to bring international talent to Canada to work on emerging AI solutions by providing access to capital, networks, education and mentors. "NextAI is proud to work with a digital leader like Scotiabank to advance AI technology and young entrepreneurs in Canada," says Anthony Lacavera, Co-Chair of Next Canada. "We have incredible talent and capabilities in this country, and Scotiabank is a key driver in maintaining our place as a world leader in machine learning."

In an effort to forge partnerships in a sector that will affect every industry, some of Canada's largest corporations are contributing to a $5 million fund for artificial intelligence startups. At a panel Wednesday morning to celebrate the launch of the program, called NextAI, executives from Royal Bank of Canada, Magna International Inc., Bank of Nova Scotia and the Business Development Bank of Canada laid out their vision for how artificial intelligence can transform their businesses. They're providing funding with no strings attached to the program, which will provide artificial intelligence startups with $200,000 in addition to access to technology, mentorship and education. Artificial intelligence, also known as machine learning or deep learning, is a method of training computers to learn like children by processing huge sets of data with software that mimics the neural networks in the human brain. Machine learning is already powering algorithms that allow Netflix Inc. to predict what a user will want to watch next, help doctors diagnose diseases by comparing thousands of similar medical images and help autonomous vehicles decide how to react to objects in their paths.