AI for Banks and Credit Unions in Canada

When banks and credit unions call us

• Model risk asks where generative AI fits. Staff tools, vendor features and internal pilots need a place in the model inventory before E-23 takes effect.
• AML analysts spend their day assembling cases. Pulling transaction history, KYC data and prior alerts into one narrative takes longer than the judgment itself.
• The contact centre answers the same questions every day. Card replacement, hold periods and product features come in by phone, chat and email.
• Staff already use ChatGPT, Copilot or Claude. Compliance wants approved tools, a written policy and a record of which data may go in.
• A credit union board wants a position on AI. The team is small, and the plan has to fit provincial rules and a modest budget.

What we automate for banks and credit unions

AML case preparation. AI gathers the alert, transaction history, KYC profile and prior cases into one file and drafts the case narrative. Analysts review the evidence and write the conclusion. The compliance officer decides on any suspicious transaction report.

Onboarding and KYC documents. Document processing reads ID, incorporation and beneficial ownership documents, fills the onboarding record and flags gaps. Staff verify identity and approve the account.

Member and customer service assistant. A customer service assistant answers product and process questions from approved content, in English and French. Account-specific requests go to staff, with the conversation summarized.

Staff knowledge assistant. An internal assistant searches policies, procedures and rate sheets and cites the source. Branch and back-office staff get answers without searching the intranet by hand.

Credit file preparation. AI summarizes financial statements and supporting documents for commercial or consumer files and lists missing items. The lender or adjudicator makes the decision.

Model inventory entries for AI tools. For each AI tool in scope, we draft the inventory entry: purpose, data, owner, proposed risk rating and monitoring plan. Model risk management approves.

Canadian rules that shape the work

OSFI Guideline E-23, Model Risk Management. Published September 11, 2025 and in effect from May 1, 2027 for all federally regulated financial institutions. Its definition of a model includes AI/ML methods. It expects a model inventory, a risk rating for each model, review independent from development, and monitoring standards. Each AI use case in the roadmap gets a draft inventory entry and a proposed risk rating.

OSFI B-10 and B-13. B-10, in effect since May 1, 2024, says the institution retains accountability for activities outsourced to a third party and should set cloud-specific requirements. B-13, in effect since January 1, 2024, asks for a technology and cyber risk management framework. Vendor selection runs through both.

FINTRAC and the PCMLTFA. Banks, credit unions, caisses populaires and trust and loan companies are reporting entities. Each must run a compliance program with an appointed compliance officer and submit suspicious transaction reports as soon as practicable once reasonable grounds to suspect are established. AI supports the analyst; the determination stays human.

Provincial rules for credit unions. In Ontario, FSRA's IT risk management guidance (in effect April 1, 2024) asks credit unions to notify FSRA of a material IT risk incident no later than 72 hours after determining it occurred. We found no AI-specific FSRA guidance for credit unions as of September 2026. In Quebec, the AMF Guideline for the Use of Artificial Intelligence (March 2026) takes effect May 1, 2027 for financial services cooperatives and authorized deposit institutions and asks for an inventory and risk rating of AI systems.

Quebec Law 25, section 12.1. A decision based exclusively on automated processing of personal information must be disclosed to the person, with the main factors on request and a chance to have staff review it. Credit and account decisions keep a person as the decision-maker.

The AI compliance review turns these points into a checklist for each use case.

Which package fits

Most institutions start with the AI Opportunity Audit, because AML, service, lending and operations each have candidates and model risk needs to see the whole list. A smaller credit union can start with the Readiness Review.

Full scope of each package is on the pricing page.

How the work runs

The work follows the Remolda Cycle: Audit → Strategy → Implement → Empower → Evolve.

1. Audit. Interviews with operations, the AML compliance officer, model risk, IT security and a business line lead. We review process maps, anonymized case samples and the list of tools in use. Customer records stay in your systems.
2. Strategy. Ranked use cases, a 12-month roadmap and a draft inventory entry and risk rating for each, reviewed with model risk and compliance.
3. Implement. A six-week build of one workflow in your tenant, with the evidence model risk needs for its review.
4. Empower. Training for the teams that use the workflow and a staff AI use policy.
5. Evolve. Monitoring against the agreed standards and a quarterly review of the next use case.

In our experience the audit usually takes two weeks from kickoff. It depends on interview availability, internal security review of our access and your model risk calendar.