Pavel Abdur-Rahman is all-in on artificial intelligence. Through his work with IBM, he’s had a front-row seat to how Canadian banks are implementing AI and Advanced Analytics. We asked him about conversational AI, the next big Fintech challenge, and what role Canada plays in the future of cutting-edge AI development.
To learn more, join Pavel and other fintech leaders at Mobey Day in Toronto, August 30–31, 2017.
Carta Worldwide: What is Augmented Intelligence exactly?
Pavel: Augmented Intelligence was first coined in the 1950s by cybernetics and early computer pioneers. But now with the latest development in learning algorithms, the digitization of everything and the explosion of data, it’s gaining new meaning. Augmented Intelligence is essentially augmenting the abilities of high-end knowledge workers by helping them make better and faster decisions with data. It’s a compliment to knowledge workers, because it will take away non-value added tasks—not jobs—and create additional capacity for creative thought, better judgment calls, and an opportunity to build deeper relationships.
Carta: What is conversational AI and why is it important for Canadian banks?
Pavel: Conversational AI is like an intelligent assistant that goes beyond being an order-taker, to being a true conversation partner that perhaps challenges your assumptions.
At IBM we’re applying generic AI capabilities—both proprietary and open-source—with industry domain partners to build “multimodal vertical AI solutions” for enterprise users. Generic AI isn’t very useful. It’s like asking an engineer to perform surgery, or a lawyer to cook.
Multimodal Vertical AI means we are talking about incorporating different modes of data—structured rows and columns of data, news, social media, images, videos, conference calls, etcetera—and using it for a specific domain to accomplish a specific group of tasks like investment research, relationship management, geological exploration, or asset maintenance.
Carta: Can you give an example?
Pavel: In wealth management, advisors have to prepare for client meetings by understanding their client’s goals, their portfolio, the market. That could take a few hours of research. But that advisor could instead type their question to Watson, and have a five-minute interaction that gives them better insights than if they did four hours of solo research. It’s like having a conversation with a very smart friend, who is an expert, always learning, always available and never goes on vacation. It’s also an augmenting process versus AI doing your job for you. It could never do the job of the advisor, but it’s enabling the advisor to offer a personalized service at scale.
Carta: What benefit will this have for the financial services industry?
Pavel: We think the value proposition here will translate to hundreds of millions of dollars for a typical organization—based on their execution strategy—in revenue growth, cost reduction, improved digital experience, and scaling expertise across the organization.
It will mean a better digital experience for the advisor and for the end consumer. It will mean revenue growth through AI recommending the right products and services to the right person at the right time. It will mean cost reduction through labour arbitrage in the back-end, and advisors chasing less paperwork. It will also mean making every single advisor a world-class expert through AI.
But it’s going to have an impact way beyond financial services. Whether helping geologists find more gold with less exploration budget, or oil sands production engineers to optimize across the value chain, or maintenance crew to predict the next failure, or provinces to decrease tax revenue leakage from the underground economy, or public policy analysts to conduct more efficient jurisdictional scanning. We’re seeing parallels to all these intelligent assistants, and the self-learning algorithms are being used across industries.
Dr. Pedro Domingos at the University of Washington believes that the AI race may not be won by industry leaders alone. Similar to humans, machines seem to learn best through cross-disciplinary studies, applied to dozens of very different challenges rather than a single problem.
Carta: What do you see as the next big challenge for fintech?
Pavel: I think the biggest challenge for fintechs will be “how do B2B fintechs actually scale to enterprise organizations?” If you’re a fintech for consumers, I think the game is easier. But for enterprise buyers, how do you get to them in an efficient manner? How do you scale? That’s a tricky problem. In my humble view, the best approach might be an ecosystem framework. Like many other entities, IBM is doing its part to foster incubation, innovation and scaling partnerships for the fintechs. There’s a win-win-win possibility there, but it has to make sense for all parties involved. I’m open to suggestions!
Carta: What role do you see Toronto and Canada playing in the future of AI?
Pavel: We have the opportunity to make Toronto, and Canada, the mecca of AI, simply because three of the four “godfathers” of AI are from Canada: Geoffrey Hinton (U of T), Yoshua Bengio (U of Montreal) and Richard Sutton (U of Alberta). These three individuals have significantly contributed to deep learning, which has been a huge step in AI development in the last five years. This has attracted significant foreign investments from Google, Facebook, Baidu, Uber, and others. While there are always risks of brain drain and IP leakage, the current policies related to innovation, R&D tax credit, and the US’s political climate, mean Canada has a wonderful opportunity to be the centre of AI universe.
Join Pavel Abdur-Rahman and other fintech leaders in Toronto at Mobey Day 2017, on August 30–31. He’ll be speaking about conversational AI for banks. Register online.
You can connect with Pavel and follow him on LinkedIn here.