The Complete Guide to Using AI in the Financial Services Industry in Canada in 2025

Canada's financial sector is at an inflection point in 2025: federal guidance calls for responsible, fair and transparent AI adoption in the public service (Government of Canada federal AI strategy (responsible use of AI)), while industry forums led by OSFI and the Global Risk Institute are sounding alarms about security - deepfake attacks alone have surged roughly twentyfold in recent years - alongside practical opportunities to boost efficiency and customer service (OSFI & Global Risk Institute FIFAI workshop report on AI threats and opportunities).

Regulators and banks are pushing governance, vendor oversight and data controls as preconditions for scaling generative and agentic AI, and firms that couple disciplined risk frameworks with workforce training will move fastest.

For Canadian teams ready to build practical skills that meet both innovation and control needs, the AI Essentials for Work bootcamp offers a 15‑week, applied pathway to learn prompts, tools and workplace use cases (AI Essentials for Work bootcamp (15-week applied AI skills for the workplace)).

Public-private forums make sense for learning together and finding ways to bring the benefits of using AI with appropriate controls and risk management. - Angie Radiskovic, Deputy Superintendent, OSFI

For Canadian financial firms, artificial intelligence is already more than a buzzword - it's a set of technologies that perform tasks that normally require human intelligence, from spotting fraud to answering customer questions with a virtual assistant (Financial Consumer Agency of Canada guidance on artificial intelligence in banking).

Generative AI is a specific class of models that creates new content - text, code, images or audio - based on a user “prompt,” and it's being trialed across banks for drafting communications, powering contact‑centre assistants and helping developers write code (Treasury Board Secretariat guide to the responsible use of generative AI; CIBC generative AI pilots and business impact).

The upside is tangible: improved productivity, faster customer service and stronger analytics; the downside is real too - privacy leaks, cybersecurity exposure, hallucinations and misuse such as fraud or misinformation are all documented risks, as noted by the Financial Consumer Agency of Canada and the Treasury Board Secretariat.

Central banks are already using AI to forecast inflation and clean regulatory data, underscoring that these tools affect both operations and macro policy (Bank of Canada remarks on artificial intelligence, the economy and central banking).

A vivid reality check: some observers note a single GenAI query can use many times the energy of a typical web search, so environmental and infrastructure costs matter as much as governance.

In short, Canadian firms need to treat AI and GenAI as practical toolsets - defined, scoped, tested and documented - rather than magic solutions.

When you enter a dark room, you don't go charging in. You cautiously feel your way around. And you try to find the light switch. - Tiff Macklem, Bank of Canada

Across 2024–2025 Canadian banks, insurers and capital‑markets firms have moved from pilots to practical deployments of AI to tackle fraud, AML and customer service: insurers are pooling de‑identified claims and using industry‑wide AI analytics to spot suspicious patterns that individual carriers can't see on their own (Shift Technology: CLHIA pooled-data fraud detection for Canadian life and health insurers), while banks and investigators are adopting targeted, behaviour‑driven models and GenAI copilots to cut alert review time dramatically - Verafin reports up to a 90% reduction compared with legacy approaches - and to move beyond noisy rules toward context‑rich detection across institutions (Verafin report: Canadian financial crime trends and AML technology 2025).

Capital markets regulators and researchers are also flagging investor‑facing use cases - decision support, automation and scams/fraud - so firms must balance rapid deployment with explainability and investor protection (Ontario Securities Commission guidance: AI in capital markets).

The result is a pragmatic shift: AI is now a standard tool for real‑time interdiction, consortium analytics, behavioural biometrics and NLP‑driven review, but success depends on clean data, cross‑firm collaboration and governance to keep false positives down and customer trust intact - after all, Canadians face nearly a billion dollars in consumer fraud losses, so faster, smarter detection isn't a nice‑to‑have, it's essential.

“Using artificial intelligence to identify potential fraud has proven incredibly beneficial for individual insurers.” - Jeremy Jawish, CEO and co‑founder, Shift Technology

Canada's federal regulatory picture for AI remains defined by two competing realities in 2025: a detailed, risk‑based blueprint that would have reshaped how firms manage “high‑impact” systems, and a political reset that left that blueprint in limbo.

The government's companion document for the Artificial Intelligence and Data Act laid out core elements that matter for financial services - definitions of high‑impact systems, tailored obligations across the AI value chain, required accountability frameworks, an AI and Data Commissioner with audit and enforcement powers, and criminal and administrative penalties - tools intended to bring Canadian practice closer to international norms (ISED AIDA companion document (Artificial Intelligence and Data Act)).

But after Parliament was prorogued in January 2025 the bill containing AIDA was effectively terminated, leaving institutions and regulators watching an uncertain next act (Montreal Ethics op‑ed on AIDA's demise and next steps for AI regulation in Canada).

Practically speaking, financial firms should treat the Act's design choices - risk‑based oversight, role‑specific obligations, and accountability documentation - as the likely direction of travel, even as provincial initiatives, sectoral regulators and international alignment (noted in global trackers) fill the gap; the result is a pause that feels like an empty compliance checklist on a busy desk, and firms that act now to map responsibilities will avoid scrambling later (White & Case global AI regulatory tracker for Canada).

“The Committee should allow sufficient time for stakeholders to analyze and provide additional commentary on these new amendments. Still, what is before the committee is a deeply flawed legislative framework on a pivotal matter for all Canadians.”

For Canadian banks and insurers the practical risk taxonomy from the OSFI‑FCAC risk report boils down to a familiar set of internal and external threats - data governance, model risk and explainability, legal/ethical and reputational exposure, third‑party concentration, plus operational and cybersecurity vulnerabilities - while external risks now include sophisticated AI‑enabled fraud (for example, deepfakes and voice cloning that have driven many institutions to rethink voice authentication).

The operational playbook is equally clear: treat AI as a lifecycle risk that needs multidisciplinary oversight, rigorous data quality controls, continuous monitoring and human‑in‑the‑loop checkpoints, plus formal third‑party due diligence and contingency planning; practical tools such as the Government of Canada's Algorithmic Impact Assessment help score impact levels and tie mitigation requirements to deployment decisions.

Institutions should translate these controls into board‑level accountability, documented testing and audit trails, privacy impact assessments and ongoing model explainability reviews so that adoption delivers efficiency without amplifying systemic or consumer harm - the OSFI‑FCAC analysis shows most firms are already scaling AI, but the difference between safe rollout and costly failure is disciplined governance, not luck (OSFI‑FCAC AI Uses and Risks Risk Report for Federally Regulated Financial Institutions; Government of Canada Algorithmic Impact Assessment (AIA) tool).

Practical governance for Canadian financial firms means turning high‑level principles into repeatable habits: embed AI oversight into existing board and risk committees, require model inventories and versioned audit trails (treat model changes like ledger entries), and use risk‑based tools such as the Government of Canada's Algorithmic Impact Assessment and broader responsible‑AI guidance to score impact and map mitigations (Government of Canada TBS Responsible Use of AI and Algorithmic Impact Assessment).

Make documentation non‑optional - deployment playbooks, explainability reports, privacy/PIA records and vendor due‑diligence files - so decisions are defensible and auditable; FIFAI experts stress extending existing governance rather than inventing a parallel bureaucracy, and recommend clear roles, a defined risk appetite and flexible policies as adoption matures (OSFI FIFAI responsible AI guidance for financial industry governance).

Workforce measures matter: mandatory AI literacy and role‑specific retraining, multidisciplinary review panels (legal, privacy, domain experts, data scientists), and accessible courses for staff build the human‑in‑the‑loop that regulators and customers expect; one vivid way to think about it is this - a well‑documented model with named reviewers and changelogs feels as trustworthy as a stamped bank ledger, and that traceability is often the difference between a safe pilot and a regulatory headache.

"It was developed using a collaborative approach, has a well-defined scope and application, is risk-based, and it implements the principle of proportionality, as it determines a score based on the impact to customer,"

Canada's Financial Industry Forum on Artificial Intelligence (FIFAI) Workshop 1 made one thing clear: AI is both a force multiplier for defence and a force multiplier for attackers - deepfake attacks have surged roughly twentyfold and participants warned AI has moved from “possibility” to an “automation” phase, enabling millions of concurrent incidents if unchecked.

The workshop flagged social engineering and synthetic‑identity fraud as the single most acute AI‑related challenge (71% of participants), followed by deepfake identity fraud (40%), while 60% said the speed of AI advancement outpaces existing risk management and 56% cited third‑party vetting as a top internal hurdle; practical priorities that emerged include strengthening identity verification and employee training, adopting MFA and zero‑trust baselines, deploying AI‑assisted anomaly detection and adversarial testing for model integrity, and tightening vendor due diligence and contractual transparency to reduce supply‑chain concentration.

FIFAI participants urged use‑case driven adoption (measureable business impact), better information sharing across firms and regulators, and investment in digital identity and AI monitoring tools so institutions can turn these threats into managed risks - for a full read, see the OSFI FIFAI workshop on security and cybersecurity and the Cyber Centre update on AI-enabled threats to democratic processes.

Public-private forums make sense for learning together and finding ways to bring the benefits of using AI with appropriate controls and risk management. - Angie Radiskovic, Deputy Superintendent, Office of the Superintendent of Financial Institutions. May 2025

Canada's 2025 AI outlook in financial services is pragmatic rather than breathless: adoption is accelerating where the ROI is clear - fraud detection, customer service and cybersecurity - yet barriers remain in data, governance and compute capacity.

Industry surveys show near‑universal readiness (98% of banks say they're AI‑ready) with many already implementing projects (63% actively deploying) according to GFT's Canadian Banking Disruption Index, while PwC highlights growing interest in agentic AI and warns that only a small share of CEOs expect deep, systematic integration within three years - so momentum is strong but uneven (regulatory headwinds and political uncertainty in Canada's 2025 regulatory outlook for financial services and technology are tempering some plans).

Market forecasts point to rapid expansion - Credence Research projects a CAGR of 27.4% for AI in finance through 2032 - so firms that solve data quality, shore up governance and secure compute (Canada's per‑capita compute lags peer countries, turning training cycles from hours into days) will capture disproportionate value; in short, the winners will be the teams that move beyond pilots to repeatable, well‑governed AI playbooks and measurable use cases (PwC Next in Canadian Banking 2025 report, GFT Canadian Banking Disruption Index 2025, Credence Research Canada AI in Finance market forecast).

“When you enter a dark room, you don't go charging in. You cautiously feel your way around. And you try to find the light switch.” - Tiff Macklem, Bank of Canada

Picking “the best” Canadian city for AI depends on what a financial team needs: for raw specialist talent and commercial scale, Toronto leads - CBRE's Scoring Tech Talent data shows Toronto hosting the largest pool of AI‑specialty workers (about 11,700) and adding roughly 95,900 tech jobs between 2018–2023, with over 15% of that AI talent embedded in finance, making it ideal for banks and fintechs that need local engineers, product teams and venture activity (CBRE Toronto Global report on Toronto specialized AI talent).

Montreal is the research powerhouse - home to Mila, heavyweight academics and SCALE AI - and offers deep R&D collaboration for models and supply‑chain AI work, while both cities are strengthening the physical backbone (Equinix documents multiple Toronto and Montreal IBX data centres and federal infrastructure investments that are critical for AI workloads) (Equinix: The rise of AI in Canada - building momentum for the intelligent age).

Vancouver and Alberta (including Edmonton's Amii) provide growing ecosystems and attractive costs for hiring or satellite teams, but firms should note the persistent talent squeeze: hiring data shows demand for finance and tech skills remains high in 2025, with many employers turning to contract hires specifically for AI implementation and upskilling to bridge gaps (Robert Half 2025 Canada hiring trends for finance and accounting roles).

In short: choose Toronto for scale and market proximity, Montreal for cutting‑edge research partnerships, and consider Vancouver/Alberta when cost, regional talent pools or specific research institutes match your use case.

Next steps for Canadian financial teams are practical and urgent: map your high‑value AI use cases, run risk‑based assessments (including the Algorithmic Impact Assessment and FASTER principles in the Treasury Board Secretariat's guide to the responsible use of generative AI), tighten vendor and model governance, and make workforce literacy mandatory so human reviewers can spot bias, hallucinations and security gaps before they affect customers; regulators and advisors also point to federal compute and safety investments as a catalyst for scaling responsibly (see analysis of Canada's recent AI funding and infrastructure commitments).

Treat documentation like a stamped bank ledger - named reviewers, versioned changelogs and transparent user notices - and couple that traceability with stronger identity verification, adversarial testing and vendor audit rights to reduce fraud and supply‑chain concentration.

Finally, invest in practical upskilling now: short, applied programs that teach promptcraft, use‑case testing and operational controls can move teams from pilots to repeatable production safely (consider the AI Essentials for Work bootcamp to build role‑specific skills and prompts for service, compliance and fraud use cases).

“FASTER: Fair, Accountable, Secure, Transparent, Educated and Relevant” - Treasury Board Secretariat guidance on using generative AI in the public service.