Top 5 Jobs in Financial Services That Are Most at Risk from AI in Canada - And How to Adapt

Canada's financial services sector is at an inflection point: OSFI and FCAC report AI use climbed from roughly 30% in 2019 to 50% in 2023 and is projected to reach about 70% of federally regulated institutions by 2026, bringing big gains - and big new risks for staff on the front lines (OSFI–FCAC report on AI uses and risks in federally regulated financial institutions).

Regulators and industry forums warn that AI amplifies cybersecurity, third‑party and model risks, and even fuels hyper‑personalized fraud - deepfake attacks have surged roughly twenty‑fold - so roles from tellers to junior analysts must learn practical controls, prompt design and risk‑aware workflows.

For workers who need hands‑on, job‑relevant training, Nucamp's 15‑week AI Essentials for Work bootcamp teaches how to use AI tools, write effective prompts, and apply safeguards across common banking and insurance tasks (Nucamp AI Essentials for Work registration), turning regulatory pressure into concrete workplace advantage.

“Today's forum is a great step toward a better understanding of AI, its role in the financial industry, and how to think about security and cybersecurity risks. A better understanding can dispel unfounded fears and enable us to focus on real problems and to identify tailored solutions.” - Suzy McDonald, Associate Deputy Minister, Department of Finance Canada. May 2025

Methodology leaned on complementary, peer‑reviewed lenses to single out the Top 5 at‑risk roles: Statistics Canada's complementarity‑adjusted AI occupational exposure index (Mehdi & Morissette) mapped occupations against 2016 and 2021 census data and O*NET task profiles to flag where AI can technically perform key functions, while IRPP's generative‑AI study used OaSIS and ChatGPT to score skills and work activities for near‑term automation risk - together these approaches capture both

“can AI do this task?”

“is this task likely to be complemented or replaced?”

(see the Statistics Canada experimental estimates and IRPP's Harnessing Generative AI for the technical details).

The combined signal prioritizes jobs that are high‑exposure with low complementarity, high in local demand (location quotients), and concentrated in routine clerical or data‑processing activities - notably, clerical information‑handling scored about 4.29/5 on IRPP's automation scale - so roughly 60% of Canadian workers may see some degree of transformation rather than outright disappearance.

This multi‑source, task‑level method helps ensure the Top 5 list focuses on roles where AI can both perform core tasks and where labour‑market dynamics make displacement or rapid task reshaping most likely.

Bank and financial clerks - including tellers and back‑office staff who handle payments, forms and routine account updates - sit squarely in the group of roles that Statistics Canada flags as highly exposed to AI‑related transformation: experimental estimates show finance and insurance among the industries with a large share of jobs in the high‑exposure/low‑complementarity quadrant, meaning many clerical tasks could be automated rather than simply augmented (Statistics Canada experimental exposure estimates).

At the same time, Canadian firms in finance are already adopting text analytics, virtual agents and other AI tools - 30.6% of finance and insurance businesses reported AI use in Q2 2025 - and many are recasting workflows rather than immediately cutting staff: 40.1% developed new workflows and 38.9% trained employees to use AI, signaling that teller and back‑office work is likely to morph into oversight, exception handling and higher‑value customer contact rather than vanish overnight (Statistics Canada Q2 2025 AI use analysis).

The practical takeaway is clear: routine information‑handling is the focal point of change, so bank clerks who gain workflow, prompt and risk‑aware AI skills can turn disruption into a chance to move from data keying to quality control and customer problem solving - a shift as visible as a once‑crowded teller line becoming a few experts managing smart tools.

Accounting clerks and data‑entry specialists - those who still spend hours matching invoices and keying transactions - are squarely in the crosshairs of automation, but not destined for extinction: Statistics Canada's task‑based estimates show 10.6% of Canadian jobs were at high risk of automation and office‑support roles (where clerical accounting sits) had a 35.7% high‑risk share, meaning routine transaction work is the first to go (Statistics Canada automation risk estimates for Canadian jobs).

At the same time, practical industry reporting finds AI (OCR, ML and generative tools) can turn messy invoices and unstructured records into clean feeds in seconds, freeing bookkeepers to become advisors rather than keyers - Herzing notes growing demand for accounting roles even as task mix shifts, citing 23,000+ Canadian job postings for bookkeepers and accounting technicians in a recent year (Herzing analysis: AI changing accounting jobs); NetSuite and other vendors show the same pattern: AI automates touchless invoice processing and reconciliations so humans focus on exceptions, ethics and insight.

Think of it like the ten‑key fading into history while a small circle of humans reads the one weird, anomalous file the machines flag - those who learn prompt design, oversight and analytics will be the ones advising management tomorrow.

“the answer to every tax question begins with, ‘It depends…” - Herzing (on why human judgment remains essential)

Insurance claims processors and routine underwriters are squarely in the crosshairs of AI: regulators and industry surveys show claims adjudication and underwriting already rank among insurers' top AI use cases, promising faster triage and “touchless” settlements but also amplifying model, data‑governance and fraud risks (see the OSFI–FCAC risk report on core use cases and risks).

Machine vision, OCR and ML can turn messy policy forms into instant decisions, yet the very same generative tools supercharge social‑engineering and synthetic‑identity schemes - modern AI can mimic a voice from seconds of audio - so human reviewers will increasingly shift to exception handling, validation and explainability work.

The upside is tangible: pooled, explainable AI fraud detection is helping Canadian life and health insurers spot patterns across millions of claims faster than any single desk could before (CLHIA's program with Shift Technology expands industry‑wide detection).

The practical playbook is clear for front‑line claims teams in Canada: learn prompt and model‑oversight skills, insist on third‑party scrutiny and clear explainability, and treat AI as a risk‑aware assistant - otherwise one sophisticated deepfake could turn a routine claim into a systemic headache.

“Today's forum is a great step toward a better understanding of AI, its role in the financial industry, and how to think about security and cybersecurity risks. A better understanding can dispel unfounded fears and enable us to focus on real problems and to identify tailored solutions.” - Suzy McDonald, Associate Deputy Minister, Department of Finance Canada. May 2025

Contact‑centre agents at banks and insurers are on the front line of AI's customer‑engagement revolution - and its risks: tools that handle routine queries and verification are already mainstream, but regulators flag chatbots, customer verification and hyper‑personalized outreach as top use cases that must be tightly governed (OSFI–FCAC report on AI uses and risks in federally regulated financial institutions); at the same time, security experts warn that generative tools supercharge social‑engineering and identity fraud (a near‑term, acute threat), including voice cloning from only seconds of audio, so a single convincing deepfake call can turn a routine service request into a systemic incident (FIFAI Workshop 1 report on AI security and cybersecurity risks in financial services).

The practical implication for Canadian contact centres is clear: agents must shift from scripted resolution to verification‑savvy escalation, mastering AI‑aware workflows, human‑in‑the‑loop checks and indicators of synthetic identity - think of a once‑predictable Q&A queue becoming a triage line for the one strange call the machines can't trust.

“Today's forum is a great step toward a better understanding of AI, its role in the financial industry, and how to think about security and cybersecurity risks. A better understanding can dispel unfounded fears and enable us to focus on real problems and to identify tailored solutions.” - Suzy McDonald, Associate Deputy Minister, Department of Finance Canada. May 2025

Junior quantitative analysts and credit‑adjudication support in Canada are squarely in the path of powerful automation: vendors now offer configurable, AI‑driven credit‑scoring engines and collaborative approval workflows that can score and route applications in seconds, shrinking manual spreading and reconciliation into a single audit‑logged pipeline (HighRadius automated credit scoring and workflows).

Open‑banking data and alternative inputs further change the signal set used to judge borrowers, enabling richer, real‑time risk assessments that expand access yet raise transparency and fairness questions (Finicity open-banking enhanced credit scoring).

Solutions that combine automated spreading, scoring and model lifecycle controls promise speed, consistency and scale - think of a junior analyst trading hours of spreadsheet work for supervising a dashboard that flags the one risky file out of thousands - but that shift brings new responsibilities: model oversight, explainability, bias testing and exception adjudication as described by automated lending platforms and risk suites (Moody's automated spreading and scoring for lending); mastering those controls is now the career lever for staying relevant.

“Zest AI's underwriting technology is a game changer for financial institutions. The ability to serve more members, make consistent decisions, and manage risk has been incredibly beneficial to our credit union. With an auto-decisioning rate of 70-83%, we're able to serve more members and have a bigger impact on our community.” - Jaynel Christensen, Chief Growth Officer

Conclusion: a cross‑cutting adaptation blueprint for Canadian financial services workers boils down to a short, practical sprint: first map the handful of routine tasks that AI can already do (verification, invoice matching, triage) and the ones that must stay human‑owned (exceptions, explainability, fraud checks); then run a 90‑day cycle that pairs rapid learning with real workflows - weeks 1–3: learn prompt fundamentals and role prompting (clear objectives, context, examples) using practical guides like Vellum guide: How to craft effective prompts and role‑prompting patterns from LearnPrompting; weeks 4–7: prototype job‑specific prompts and dynamic templates, A/B test variants and track basic metrics (accuracy, relevance, user satisfaction) as recommended by LLMOps best practices; weeks 8–12: harden human‑in‑the‑loop checks, model‑oversight steps and vendor governance, then train peers on escalation triggers and explainability.

For workers and managers who want a structured pathway, Nucamp's AI Essentials for Work teaches these skills - prompt writing, job‑based AI tools and risk‑aware workflows - and can be paired with short pilots to make the change tangible (think: one anomalous claim or one weird file that your team goes from fearing to owning).

This blend of hands‑on prompt practice, iterative evaluation and clear governance turns regulatory pressure into a career lever rather than a threat.

“Guiding a large language model via specific prompts is critical for producing desired outputs and ensuring healthcare systems make the most out of generative artificial intelligence.” - Kenneth Harper, general manager, Dragon portfolio (used here as a practical reminder that precise prompts and oversight matter across sectors)