How AI Can Help with the Detection of Financial Crimes

How AI Can Help with the Detection of Financial Crimes

Paige Dickie develops Artificial Intelligence (AI) and digital strategy for Canada’s banking sector at the Vector Institute for Artificial Intelligence in Toronto. She began her career in management consulting — much to the disappointment of her father, an engineer — because she had earned advanced engineering degrees in biomedical and mechanical engineering. Dickie initially worked at McKinsey, the global consulting firm, helping multinational financial institutions across a range of fields from data strategy and digital transformation to setting up innovation centers. She recently joined Vector to lead what she describes as “an exciting project with Canada’s banking industry. It’s an industry-wide, sector-wide, country-wide initiative where we have three different work streams — a consortium work stream, a regulatory work stream, and a research-based work stream.”

Knowledge@Wharton interviewed Dickie at a recent conference on artificial intelligence and Machine Learning in the financial industry, organized in New York City by the SWIFT Institute in collaboration with Cornell’s SC Johnson College of Business.

According to Dickie, AI can have a significant impact in data-rich domains where prediction and pattern recognition play an important role. For instance, in areas such as risk assessment and fraud detection in the banking sector, AI can identify aberrations by analyzing past behaviors. But, of course, there are also concerns around issues such as fairness, interpretability, security and privacy.

An edited transcript of the conversation follows.

Knowledge@Wharton: Could you tell us the Vector Institute and the work that you do?

Paige Dickie: Vector is an independent, not-for-profit corporation focused on advancing the field of AI through best-in-class research and applications. Our vision is centered on developing machine- and deep-learning experts. If I were to use an analogy, if Vector were a manufacturing company, what would come off the conveyor belt would be graduate students waving machine- and deep-learning degrees.

We’re funded through three channels. We have federal funding through CIFAR, which is the Canadian Institute for Advanced Research. We have provincial funding through the government of Ontario. We’re also funded by some sponsors.

Dickie: We have a lot of banks as sponsors, but we also have a number of other companies in industries like manufacturing and health care. One of the important things to recognize about these sponsors is that they’re all located in Canada. This was a deliberate decision on our part. For one, our public and private sectors recognize the economic potential of AI. Not only that, but for those who aren’t aware, Canada happens to be home to some of the world’s most prominent and influential thinkers within the field of AI, including Yoshua Bengio from Montreal; [Richard] Sutton from Edmonton; and Geoffrey Hinton, who’s a founding member of the Vector Institute. His Toronto research lab made major breakthroughs in the field of deep learning that revolutionized speech recognition and object classification.

But, despite all this, we were losing our talent. The data scientists and computer scientists that we produced in Canada would move to the U.S. and head AI roles in major tech companies like Google, Microsoft and Apple. That’s the basis for how Vector was formed. We made a deliberate decision that our sponsors had to be located here, because studies have found that if you’re able to place a PhD researcher for around 18 months after they graduate, they typically make that place their home, because they’re at the age where they’re starting families and are growing roots in the area. If we can keep our talent in Canada for 18 months by increasing the number of options — the tech companies that are in this space, the “cool” companies that they want to work for — then we might be able to reverse that brain drain we’ve been experiencing.

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