How Canada and the U.S. Are Regulating AI in Financial Service: What Brokers Should Know

Brokers, fintechs, and realtors alike will be interested in learning more about new regulations around AI. While Canada set a new federal AI law with the Artificial Intelligence and Data Act (AIDA), the US has inserted AI into its existing financial regulations (the Equal Credit Opportunity Act, or ECOA, the Fair Credit Reporting Act, or FCRA, and UDAP, Unfair or Abusive Acts or Practices). The legacy structure means Fair Lending rules are actively enforced around AI bias in the United States, while Canada is still building out new ones. 

No matter which side of the border you’re on, it’s a good idea to stay abreast of any changes, modifications, and updates on regulating AI. Here is a quick guide for 2025:

Canada Sets a New Federal AI Law with the Artificial Intelligence and Data Act 

In 2025, Canada finalized its first national AI statute, the AIDA, or Artificial Intelligence and Data Act, in Bill C-27. AIDA had direct implications for banks, mortgage lenders, insurance companies, and fintechs. AIDA establishes mandatory risk management, incident reporting, and government expectations for AI. The United States eschewed state laws in favour of a “minimally burdensome national standard” and doubled down on existing regulation, including ECOA, FCRA, and UDAP. 

In the United States, financial systems are regulated indirectly. AI technology is included in existing financial regulation, where it is actively enforced. A fintech bank using an AI powered credit model, for example, would be covered under ECOA if it was using the technology to approve or deny personal loans. Even though it does not use race, gender, or age to power the tool, it has ECOA exposure via zip codes, employment stability checks, and education. Regulators would require model testing for bias, the removal of proxy variables, and adverse action notices. ‍

In Canada’s case, AI systems that handle financial data are subject to heightened expectations, data portability and AI governance by virtue of them being AI. AIDA explicitly links compliance to open banking reforms, while in the US, AI oversight is more consumer-protection oriented. In the States, the Consumer Financial Protection Bureau oversees consumer data rights via (CFPB)’s Section 1033 Rule. CFPB 1033 requires AI systems ingesting 1033-applicable data to be held under CFPB scrutiny. 

Canada Formally Defines “High-Impact AI Systems”

Canada formally defines “high-impact AI systems” in AIDA. These “high impact AI systems” are a legal category that does not exist in the United States – and while the exact list of AI systems included as “high impact” will vary, the statute anticipates the definition will include systems that can make or materially influence lending decisions, affect access to financial services, and create the risk of economic, psychological, or legal harm. 

In practice, AIDA captures credit scoring and underwriting, fraud detection, automated loan approvals, tenant or borrower risk or decisioning tools, and identity verification systems. Canada places a heavier emphasis on risk assessments and design of AI models prior to implementation, while US regulators are explicit about AI being used in credit decisions – institutions who use AI to make their credit decisions must explain the adverse actions and ensure their models aren’t a “black box.” 

Incident Reporting vs. Supervisory Exams

Canada’s AIDA rules emphasize mandatory reporting for AI-driven incidents and disclosure of when these AI systems cause material harm. AIDA adds internal escalation and record-keeping obligations, highlighting its focus on safe regulation instead of traditional financial supervision. In the United States, regulators focus on supervising parties, reviewing risk management models, and enforcing infractions. 

With a more systemic approach to financial regulation of AI, the US regulators have signalled that AI vendors, model providers, data brokers, and platforms are all under scrutiny – and that banks, financial institutions, and data must remain accountable. In Canada, the approach focuses more on model governance, accountability, and organizational controls. The impact of these differences in legislation mean that US regulators target Big Tech and AI vendors, while Canada goes to the financial institutions themselves.