Technology is everywhere in the financial services industry, and banks have been building their artificial intelligence (AI) bona fides in recent years.

Components of AI include natural-language processing, which uses technology to analyze the spoken and written language, and machine learning, which uses algorithms to analyze and learn from large amounts of data.

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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.” For every trade [Aiden] makes, it also justifies why it just did that,” Agrafioti said. “And that gives people confidence and trust that they understand the context of the algorithm they’re seeing.”

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.

Traditional electronic trading programs are designed to follow specific rules in certain market conditions and must be reprogrammed when they encounter new scenarios — which leads to downtime. Aiden, on the other hand, is dynamic: it can adapt to changing market conditions, Agrafioti said.

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Still, Aiden is not completely autonomous. As with other AI platforms, humans can overrule Aiden’s investment decisions when necessary. But before that happens, Aiden has a chance to explain its rationale to a human supervisor — a crucial feature for an AI platform.

Banks also are using AI to leverage one of their most valuable resources: client data. These data may include emails sent to an advisor, financial transactions and the number of times a client has signed in to an online account.

In 2020, Bank of Nova Scotia announced the creation of a global AI platform that uses client data to help identify client needs.

During the first wave of Covid-19, Scotiabank used its AI capabilities to identify clients who were at risk financially as a result of the pandemic. The bank then contacted those people to offer potential solutions — something Scotiabank intends to do again in the future.

“We will continue to focus on providing applicable unique solutions for our customers and being able to tailor those solutions to customer needs,” said Phil Thomas, executive vice-president, customer insights, data and analytics, with Scotiabank.

As Scotiabank and other companies continue to harness customer data, critics wonder how AI platforms use that data, leading to growing demand for “ethical” AI.

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