ATOM: RBC’s proprietary AI Foundation Model for Financial Services

​ATOM: RBC’s proprietary AI Foundation Model for Financial Services

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In this NeurIPS research talk, RBC Borealis, will introduce ATOM: RBC's proprietary AI foundation model designed specifically for financial services. This session explores the research behind ATOM's design, including its use of large-scale asynchronous transactional data to model complex client behaviours. Combining domain-specific insights with foundation model techniques, ATOM delivers predictive capabilities across a variety of baking products, channels, and tasks. By harnessing RBC's extensive data ecosystem and commitment to responsible AI, ATOM represents a significant toward generalizable, trustworthy and scaleable machine learning systems for the financial industry.

Speaker Bio:

Mohamed Osama Ahmed is a Staff Machine Learning Researcher at RBC Borealis, where his research spans Generative AI, Foundation Models, Agentic AI, and Uncertainty Estimation. He has led several flagship initiatives at Borealis, including ATOM-powered PBC.AI and Ask ATOM, which have resulted in multiple patents and publications in top-tier venues.
Mohamed holds a Ph.D. in Computer Science from the University of British Columbia, where his dissertation focused on Optimization for Machine Learning. He also earned a MASc in Electrical Engineering from UBC, a M.Sc. in Engineering Physics, and a B.Sc. in Electrical Engineering from Cairo University.

More info can be found on the NeurIPS 2025 site here.