Will AI Replace Finance Jobs in Australia? Here’s What to Do in 2025

For finance workers in Australia, "Will AI replace finance jobs?" is less a binary threat and more a practical puzzle: AI is rapidly automating repetitive work (one industry estimate puts about 36% of accounting tasks at risk), and savvy users can save roughly 46–79 minutes a day - so roles are migrating from processing to advisory work; national coverage of the Jobs and Skills Australia findings highlights that AI reshapes tasks more than whole occupations, even as examples like Commonwealth Bank's call‑centre changes show disruption is real.

Employers now routinely shortlist candidates who combine financial expertise with AI literacy, so short, workplace‑focused upskilling matters - consider an applied option such as Nucamp's Analysis of AI and Changing Skillset in Australia's Accounting Sector (Launch Recruitment), the Jobs and Skills Australia report (ABC), or a practical course like Nucamp's AI Essentials for Work bootcamp (Nucamp) to turn automation into an advantage.

“It's not jobs that are at risk of AI, it's actual tasks and skills.” - Dr. Evan Shellshear

AI is already reshaping everyday finance work across Australia by automating high‑volume, repeatable tasks and surfacing faster insights so people can focus on advisory and exceptions: banks and fintechs use machine learning and NLP for fraud detection, intelligent document processing and personalised advice, while RPA and agentic systems accelerate loan decisions and compliance checks.

Local analysis highlights that most firms are experimenting with AI in reporting and risk management, and practical use cases - from real‑time credit underwriting and automated KYC to hyper‑personalised customer recommendations - are delivering measurable cost and time savings (see the KWM report on opportunities, risks and benefits).

Appinventiv's detailed use‑case roundup maps how fintechs deploy AI across onboarding, scoring, fraud and wealth management, and agent research shows autonomous agents can clear huge alert volumes in seconds, letting humans handle the complex exceptions that still need judgement.

For finance workers in Australia the result is clear: everyday workflows are becoming faster and more data‑driven, and the competitive edge will go to teams that pair domain expertise with solid AI governance and prompt‑crafting skills.

The market for AI agents in financial services is expected to grow by 815% between 2025 and 2030.

In Australia the clearest near‑term casualties aren't high‑level finance careers but the routine, task‑heavy jobs that feed them: data‑entry clerks (the ILO flags ~70% of tasks exposed), book‑keepers, office clerks and receptionists, plus large volumes of call‑centre and back‑office roles where chatbots and document‑reading models can replace manual processing - Commonwealth Bank's recent call‑centre cuts are a concrete example.

Business and systems analysts, some programmers and sales/marketing roles show exposure too, mainly where repeatable reporting, invoice processing, KYC checks and standardised customer responses dominate the day.

Importantly, research emphasises augmentation over wholesale disappearance: many tasks will be automated while others are upgraded, so the “at risk” label really maps to specific tasks (data entry, routine reconciliation, standard query handling) rather than entire careers; see the Jobs and Skills Australia coverage and the ILO's task‑level analysis for the scorecards and timelines that matter for planning.

“The overarching message is that almost all occupations will be augmented by AI. It doesn't make a difference which sector you are in, or at what skill level: you will be influenced by AI.”

In Australia the story is clear and practical: generative AI has supercharged productivity in AI‑exposed industries - PwC finds productivity growth jumped from about 7% to 27% between 2018–22 and 2018–24 - and that uplift is showing up in pay and hiring, especially in financial services where AI‑skill postings rose sharply; workers with AI skills now command, on average, a 56% wage premium, effectively worth “more than half” extra pay for those who master these tools.

Demand for AI capabilities is reshaping job requirements fast (skills are changing roughly 66% faster in AI‑exposed roles), degree preferences are slipping slightly, and firms are prioritising workers who can combine domain expertise with practical AI fluency; that makes short, targeted reskilling a strategic move for anyone in finance who wants to move from routine processing to higher‑value advisory work.

For employers and workers alike the headline is simple: AI is boosting revenue per employee and wages, but the payoff requires deliberate upskilling to avoid an emerging skills divide.

Read the PwC analysis and local coverage for the evidence on where to focus.

“Those who are mastering, and driving new value from this technology can command higher wage premiums, in some cases, upwards of 56%.” - Tom Pagram, PwC Australia's Artificial Intelligence and Global AI Factory Leader

Real Australian case studies make the trade‑offs unmistakable: large banks show both the upside of AI and the cost of rushed rollouts - CommBank's AI assistant “Ceba” has been credited with cutting call‑centre wait times by around 40% and partnerships with firms like H2O.ai underpin big fraud‑reduction and automation wins, yet a July 2025 move to replace 45 call‑centre roles with an AI voice bot was publicly reversed after the bot struggled with volume, customers faced long delays and the bank began rehiring or redeploying staff; the episode, widely reported by the ABC and analysed by Twenty44, underlines the simple “so what?”: efficiency gains are real, but without staged pilots, worker consultation, clear governance and upskilling they can blow up into reputational and service failures, so Australian finance employers should pair technical pilots with workforce‑first change management to capture the upside without repeating the same costly mistakes (ABC News: Commonwealth Bank AI-related job cuts report, Twenty44 analysis: Commonwealth Bank AI job replacement mistake, H2O.ai case study: CBA AI capabilities and fraud reduction).

“CBA's AI misstep isn't proof that AI doesn't work. It's proof that AI without people doesn't work.”

Redesigning finance roles in Australia means deliberately shifting work from machines back to people - take the repeatable pieces (invoicing, data matching, routine KYC) and let AI or RPA handle them so each accountant or analyst can spend time on judgement, narrative and client strategy; practical pilots, clear governance and targeted training make that transition safe and fast.

Start small: prove an automation, measure the time saved (Pearson models suggest 4.5–7 hours a week per worker), then reallocate that capacity into advisory, controls, model‑validation and cross‑functional projects with IT and data science.

Train for T‑shaped roles (domain + AI fluency), bake ethical checks and explainability into every rollout, and pair each pilot with workforce‑first change management so efficiency gains don't become service failures.

Policymakers and firms should back this with coordinated reskilling and infrastructure investment to turn algorithms into opportunity rather than disruption - see the Treasurer's roadmap for skills and measured adoption and the industry analysis on GenAI's potential value to finance for practical context.

“By starting the process of role redesign now, businesses can close skills gaps faster, improve retention and strengthen their competitiveness.” - Craig McFarlane

Finance roles in Australia now demand a tight blend of technical chops and human strengths: solid AI literacy and tool familiarity so teams can automate routine reporting and free up about an hour a day for higher‑value work; strong data interpretation and storytelling to turn AI outputs into commercial advice; automation and process‑optimisation skills (RPA, prompt design and workflow mapping) to scale repeatable work safely; ethical AI, explainability and data‑privacy know‑how to meet governance expectations; and cross‑functional collaboration plus empathy and communication to lead clients and stakeholders through change.

Employers are explicitly redesigning entry‑level roles and prioritising tech‑savvy hires, so focus on practical, role‑based upskilling rather than abstract theory - see the Moir Group's Top 5 AI skills for finance and the Launch Recruitment's guide to how AI is reshaping accounting and finance for practical next steps.

“AI is automating routine tasks … but crucially, it's also elevating the nature of entry-level work, making it more dynamic and skill-intensive for new entrants … companies are rethinking their traditional entry-level responsibilities.” - Damien Andreasen

Employers and policymakers should treat AI in finance as a governance and workforce challenge first: adopt the Voluntary AI Safety Standard's 10 guardrails and the government's responsible‑use approach to test, monitor and scale pilots rather than rushing full rollouts; embed Australia's AI Ethics Principles so systems are fair, transparent and contestable (privacy, explainability and human oversight must be non‑negotiable); classify employment‑facing AI as high‑risk and require meaningful worker consultation, independent audits and clear accountability lines so tools don't misapply Australia's complex payroll, recruitment or surveillance rules; and back these rules with funded reskilling, a code of practice on psychosocial safety and interoperable regulation that allows local innovation to scale safely.

Taken together, these steps turn AI from a headline risk into a managed productivity gain - think staged pilots with human checkpoints, not one‑step replacements that can create costly service or compliance failures.

Action in 2025 should be fast, practical and Australian‑focused - a six‑point plan: 1) pick one high‑volume pain point and run a small pilot (real‑time forecasting or automated reconciliations are good starters); 2) get role‑based training fast - start with a short CA ANZ Introduction to Generative AI in Finance microcourse (Australia) to demystify GenAI and build immediate CPD‑credit skills (CA ANZ Introduction to Generative AI in Finance microcourse (Australia)); 3) target the Moir Group guide to upskilling financial professionals in AI's practical skillset - AI literacy, data storytelling, automation and ethical use - rather than theory alone (Moir Group guide to upskilling financial professionals in AI); 4) embed ethics and explainability from day one, aligning with CPA Australia's Career Crossroads six‑skill framework so governance isn't an afterthought (CPA Australia Career Crossroads six‑skill framework); 5) create cross‑discipline squads (finance + IT + data) to turn pilot wins into repeatable workflows; and 6) measure time saved and business value, then scale what clearly reduces risk and boosts decision speed - the goal is not magic but measurable gains, like producing higher‑quality work in a fraction of the time, not replacing judgement.

This sequence keeps workers central while converting automation into advisory capacity.

“As the saying goes, AI won't take your job, but the people who can use it will.” - CPA Australia

The clear takeaway for finance workers in Australia is pragmatic: AI will reshape work more than it will erase it, and Jobs and Skills Australia's modelling - reported in outlets like Jobs and Skills Australia modelling in The Guardian and explored in detail at analysis in The Conversation on AI and jobs - shows higher overall employment by 2050 in AI scenarios, even if the transition brings a near‑term bump in unemployment and big shifts across occupations; routine clerical, bookkeeping and entry‑level tasks are most exposed while roles demanding interpersonal judgement and manual trades are likeliest to grow.

That means the practical focus for finance teams is twofold: protect service quality and jobs during pilots with staged rollouts and governance, and invest in fast, role‑based reskilling so time saved by automation becomes advisory capacity - not unemployment.

A national leadership framework and expanded, job‑focused training are recommended by the reports, and employers can plug short skill gaps with applied courses such as Nucamp's AI Essentials for Work bootcamp (15-week workplace AI course), which teaches prompt design and workplace AI tools in 15 weeks; think of this moment as a relay race where AI hands off routine tasks so humans can run higher‑value stretches, provided policy, employers and workers train together to catch the baton.

“In all three scenarios, there were more Australian jobs by 2050 in a world with AI than without.”