This TMU prof helps set Canada's AI rules for the financial sector
Alexey Rubtsov, a TMU math professor working with the Department of Finance, is leading the national file on artificial intelligence across Canada's financial sector. His mandate: help the federal government guide how banks and insurers use AI, so innovation doesn't outpace control.
He blends math, computer science, finance, and policy. That mix lets him separate signal from noise and convert technical nuance into policy that institutions can act on. As he puts it, "Understanding the underlying reality is where my knowledge of mathematics and technical background helps."
Why finance should care
AI decisions now hit core risk categories: credit, market, operational, cyber, and conduct. Rules that emerge from this work will influence model governance, third-party risk, explainability, and oversight across banks, insurers, and payment players.
The Department of Finance is coordinating with key regulators like the Office of the Superintendent of Financial Institutions (OSFI), the Financial Consumer Agency of Canada (FCAC), and FINTRAC. It's also comparing notes with peers in Singapore, the U.S., the U.K., and the European Union.
What he actually does
Rubtsov acts as a bridge between technical teams and policy leaders. He meets with banks and insurers to learn where AI is used, what breaks, and what controls work in practice.
He then advises on how existing rules, guidance, and supervisory tools can support safe adoption. Think of AI like cars: building the engine isn't enough-you also need clear "traffic rules." His focus is realizing AI's upside while managing downside risk.
"There are certain problems that can arise if AI is adopted recklessly," he says. "You can expose a company's or bank's systems to risks and create vulnerabilities that are hard to detect because the technology is not yet fully understood."
Why his background matters
AI in finance sits at a four-way intersection: technology, math, markets, and policy. Rubtsov speaks all four languages. That means fewer blind spots, better questions, and practical guidance institutions can use.
"It's really hard to develop sound policies if you don't understand the underlying technology," he notes. "One of the good things I do for the department is bridging the gap between technical understanding and policy development."
National and international impact
This work informs federal policy that will touch every Canadian-through pricing, access, fairness, and the resilience of the financial system. The Department of Finance is engaging closely with OSFI, FCAC, and FINTRAC, while comparing approaches with global partners.
One tangible outcome: the Financial Industry Forum on Artificial Intelligence (FIFAI), Canada's first national platform for government, regulators, and industry to discuss AI adoption across the sector. The interim report from its first workshop covered security and cybersecurity, with more workshops scheduled on financial crime, consumer protection, and financial stability.
What banks and insurers should do now
- Inventory and classify AI/ML models across business lines. Tie each to a clear use case, owner, data sources, and risk tier.
- Tighten model risk management: independent validation, challenger models, performance drift monitoring, and clear decommission rules.
- Strengthen data controls: lineage, access, quality checks, and bias testing. Keep audit trails from data to decision.
- Secure the AI pipeline: code, models, prompts, APIs, and dependencies. Treat model artifacts like code-versioned and monitored.
- Manage third-party exposure: vendors, foundation models, external data, and cloud. Contract for transparency and testing rights.
- Build explainability into high-impact decisions (credit, claims, fraud flags). Make outputs reviewable by humans with authority to override.
- Clarify accountability: board oversight, management responsibilities, and escalation paths for incidents.
- Run controlled pilots with guardrails before scaling. Measure business value and risk outcomes side by side.
From classroom to policy
Rubtsov brings real cases and policy debates into the TMU classroom. Students see how math and AI tie to concrete choices regulators and institutions must make. As he tells them: you can't regulate what you don't understand-and math sits at the core.
Where to keep an eye
Watch for outputs from FIFAI workshops and further guidance from Canadian regulators. For supervisory context, review OSFI publications and speeches on model risk and AI.
OSFI - Office of the Superintendent of Financial Institutions
Related resource for finance teams
Want a quick scan of practical tools you can test in controlled pilots? Start here:
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As Rubtsov puts it, the impact of this work is "immediate and visible." The goal is simple: keep innovation moving, keep risks contained, and keep trust intact.
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