AI Research

RBC Borealis conducts research in artificial intelligence for financial services at one of the largest banks in the world. At RBC Borealis, we aspire to publish impactful research and build industry-leading products that serve our clients’ needs from the algorithms we create.

North Star core
scientific problems

As part of RBC, we have access to data, problems, and domain experts to drive our research agenda. We organize our research into North Star areas – guiding directions for our work and formulated as a challenging problem we are trying to solve.

Asynchronous Temporal Models (ATOM)

The goal of this North Star research direction is to develop novel machine learning technologies for asynchronous time series data, commonly found in banking and other relevant customer-centric domains. 

This line of research focuses on training machine learning models in the challenging data environment of partially-observed, multi-source, imbalanced, and asynchronous time series. This research can help build technologies that can leverage the data from multiple channels, but also work for clients that use only a subset of these channels, in a way that respects privacy and upholds principles of responsible AI.

Machine Intelligence Beyond Predictive ML (Causmos)

Our AI needs to drive responsible actions and decision-making in rapidly changing environments. With the Causmos program, we aim to build machine intelligence beyond predictive ML for financial services by conducting research in areas such as causality, out-of-distribution (OOD) generalization, reasoning and planning in large language models (LLMs) and reinforcement learning.

In financial services, ensuring our AI systems drive the right decisions for our clients amidst complex, evolving landscapes is crucial. Even when human oversight is integrated as a safety net in mission-critical tasks, AI influences real-world outcomes through human mediation.

The Causmos research program at RBC Borealis is dedicated to advancing machine intelligence beyond predictive ML in this domain. 

Our focus encompasses different facets and approaches to model actions and their consequences, including reinforcement learning, planning, causal inference, and experimental design. Additionally, we delve into crucial related areas to ensure the safety of actions, such as reasoning, interpretability, adversarial robustness, and out-of-distribution (OOD) generalization.

Non-cooperative learning in competing markets (Photon)

We build models for Capital Markets data, where particular challenges include low signal-to-noise ratio, structured prediction, and game theoretic impacts from decisions we make.

Globally, Capital Markets have gone through a paradigm shift towards complete automation through Artificial Intelligence, turning it into a highly competitive area at the intersection of statistical models from various branches of machine learning.

We believe that a principled understanding of the interactions between statistical models that operate in a common environment will soon be a key success factor for leaders in the field. The Photon north star research team plans to approach this challenge from two angles:

Atomistic: a symptom-based research stream focusing on novel solutions to challenges that are a direct consequence of a competing market: large data, high noise, non-stationary dynamics, and constrained environments

Holistic: a system-based research stream focusing on a meta-level framework for holistic properties of a competing market: local stability, asymptotic behaviour, perturbation theory, and adversarial robustness.

Research Topics

Data visualization Causality Graph representation learning Learning and generalization Natural language processing Optimization Quantitative finance Reinforcement learning Responsible AI Time series modelling Transformers Unsupervised learning

Research library

Our widely published research covers a broad range of topics including Reinforcement learning, Natural Language Processing and Time Series Modeling. Our research is made freely available to support the AI community.

View all publications

Partnerships

Our partnerships enable us to benefit from the latest in academia and transfer that knowledge into real-world impact.

View all

Careers

Join our team of researchers with backgrounds in computer vision, machine learning, and natural language processing, and with PhDs in computer science, physics, computational finance, mathematics and more. Bring your research and prototypes to life – and reimagine the future of finance.

Explore open roles