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

AI matters for financial services jobs in Australia because Generative AI adoption is set to more than double in the next three years, promising big productivity wins - and big disruption - for roles that handle repeatable tasks like document review, routine credit decisions and contact‑centre work; authoritative analyses from KWM and Sapere project industry value gains and a cumulative boost to GDP (about $48.9 billion by 2035 under a medium scenario, roughly $690 per person), while regulators warn of accuracy, governance and concentration risks that demand human oversight and new skills.

That's why practical, job‑focused training matters: Nucamp's AI Essentials for Work teaches non‑technical promptcraft and workplace AI use cases to help financial professionals adapt and stay employable - see the KWM report for the industry analysis and register for the Nucamp AI Essentials for Work bootcamp to build workplace AI fluency.

“AI has the power to significantly enhance the Australian finance industry, driving efficiency, better experiences for customers and giving local finance firms a competitive edge globally. We must harness the power of AI to unlock productivity gains across the economy...” - Diane Tate, AFIA

The top‑5 list was built by triangulating global evidence on where AI is already most effective with practical signals about which tasks Australian firms actually do - prioritising roles that are heavy on repeatable, document‑driven work, scale easily across customers, and sit in areas where firms are accelerating AI investment.

Key inputs were PwC's 2025 AI Jobs Barometer (which flags rapid skill change, industry revenue gains and a clear split between augmentable and automatable tasks), data showing that up to 30% of finance tasks are plausibly automatable by 2030, and real-world use cases - from invoice reconciliation and automated reconciliations to document processing and fraud detection - that demonstrate how quickly tasks can shift (for example, a legal‑doc system that turned work requiring 360,000 human hours into seconds).

Methodology steps: map tasks within occupations, score each by repeatability, data‑intensity and regulatory sensitivity; weight by current adoption velocity in finance and corporate finance use cases; factor in timelines (immediate/short/medium) from industry analyses; and check Australian relevance via local use cases such as claims automation and real‑time fraud detection.

The result is a pragmatic, evidence‑led ranking that also flags where upskilling - not just fear - is the logical response (see PwC's full barometer and a practical finance reality check for more detail).

“AI adoption is progressing at a rapid clip, across PwC and in clients in every sector. 2025 will bring significant advancements in quality, accuracy, capability and automation that will continue to compound on each other, accelerating toward a period of exponential growth.” - Matt Wood, PwC

Customer‑facing roles are among the most exposed in Australian finance because conversational AI, advanced IVR and chatbots can now take the lion's share of repeatable enquiries - from balance checks to basic claims status - while real‑time summarisation, speech‑to‑text and intelligent routing shrink after‑call work and transfers; industry guides show implementations from local IVR vendors to enterprise platforms and real examples like Netwealth's AI‑assisted contact centre, so estimates range from roughly two‑thirds to over 80% of interactions becoming automatable or AI‑assisted in practice.

That matters: routine online‑messaging specialists and contact‑centre agents who handle high volumes of scripted queries face real displacement risk, even as firms gain faster resolution times and lower costs.

The practical response in Australia is to shift roles toward supervision, quality‑assurance of AI outputs, empathy‑led escalation and multilingual exception handling - skills that AI struggles with - and to treat AI as a tool that reassigns human effort to complex cases rather than a simple headcount cut.

For more on Australian IVR trends see the piece on AI-powered IVR systems transforming Australian call centres, and for broader industry best practice see the Zendesk AI call center best practices guide.

“Ultimately, GenAI can augment contact centers, but it's not a silver bullet. It shouldn't replace humans entirely. Human empathy, adaptability, problem‑solving skills and judgement remain crucial for resolving complex issues and maintaining customer relationships.”

Fraud‑review analysts and routine fraud detection teams in Australia are facing rapid role change as AI shifts the fight upstream: with instant rails like the New Payments Platform, banks now need systems that score and act in milliseconds, turning what used to be manual, queue‑based reviews into real‑time decisioning and automated case triage.

Modern platforms - from Tookitaki's FinCense with agentic AI and federated intelligence to commercial offerings like Feedzai's transaction fraud solution that boosted detection by 114% in an NPP rollout - demonstrate how machine learning, behavioural analytics and network linking can detect mule chains, account takeovers and APP scams sooner and with fewer false positives; see Tookitaki's guide to fraud prevention in Australia and Feedzai's Australian case study for concrete examples.

That means routine reviewers will be redeployed into higher‑value work: tuning models, validating explainable alerts for AUSTRAC reporting, investigating complex cross‑channel networks and managing customer remediation - work that leans on judgement, regulatory storytelling and domain expertise rather than sifting repetitive alerts.

The practical takeaway is vivid: where once an investigator had minutes to halt a fraud, the new reality demands catching a suspicious transfer before it clears - or risk watching money vanish in the time it takes to blink; industry pilots like BioCatch Trust™ show how shared, behavioural intelligence can help stop scams mid‑flight.

“Fraud and scam payments are nearly always transferred to mule accounts through which the criminal funnels their profits before withdrawing them…When the sending and receiving banks involved in these transactions share intelligence to identify potential money mules, it should reduce the number of customers who fall victim to scams.” - Gadi Mazor, BioCatch

Credit underwriters, loan processors and new‑accounts clerks in Australia are squarely in AI's sights because modern systems can gulp messy loan packs, extract facts from PDFs and bank feeds, and produce consistent risk scores far faster than manual review - V7 Labs underwriting guide on AI-powered credit underwriting reports productivity uplifts (20–60%) and big drops in inconsistent credit‑policy findings when AI is applied, and many lenders see time‑to‑decision slashed by half or more so approvals that took days or weeks can now happen while a borrower is still on the phone.

AI‑powered credit scoring also widens the pool of lendable customers by safely incorporating alternative data (BAI research on automated credit scoring and alternative data shows lenders can automate 70–80% of consumer decisions), which helps regional players grow without proportionate headcount increases.

The catch for Australian firms is governance: reliable explainability, bias testing and human‑in‑the‑loop checkpoints are essential to meet compliance and fair‑lending expectations, and to turn automation into a trustable decision‑support tool rather than an opaque black box - see V7 Labs underwriting guide on AI-powered credit underwriting, BAI research on automated credit scoring and alternative data for implementation detail, and the Nucamp AI Essentials for Work human-in-the-loop design note for why keeping people in critical flows matters.

The vivid reality: a well‑implemented AI engine can convert a paper mountain into a same‑day offer - but only if teams learn model governance, exception handling and how to read AI explanations.

Back‑office clerks, brokerage clerks and settlement & reconciliation teams are squarely in the RPA crosshairs because the work they do - matching ledgers, moving files between legacy systems, fixing exceptions and closing the books - is exactly what software robots excel at: fast, rule‑based, and high‑volume.

RPA combined with AI can scrape PDFs and bank feeds, automate daily reconciliations, and stitch together disparate systems so month‑end routines that once took days can run overnight or be resolved in minutes; practical case studies show end‑of‑month processes collapsing from four days to under two hours and loan workflows falling from weeks to minutes.

For Australian firms that handle high transaction volumes across AP/AR, settlements and trade processing, the payoff is cleaner audit trails, far fewer manual errors and 24/7 processing capacity - but also a clear need to redesign roles so humans own exceptions, governance and vendor oversight, not repetitive keystrokes.

Start by mapping high‑volume reconciliation and settlement paths, pilot a bot on a single use case, and scale where reconciliation accuracy and cycle time improvement are proven (see the AutomationEdge RPA loan-processing example for finance and The Lab Consulting RPA implementation overview for finance use cases).

“The quick wins are typically in RPA.” - Laurens Tijdhof, Partner at Zanders

Routine personal financial advisers, basic robo‑advice roles and wealth administration are squarely in the automation spotlight in Australia because hyper‑personalized robo advisors - now powered by Generative AI - can craft tailored portfolios, rebalance automatically and scale advice at a fraction of traditional cost; globally robo platforms already manage over $1 trillion in assets, a clear signal that commoditised, repeatable advisory tasks are shifting to machines.

For Australian firms this means the day‑to‑day work of onboarding, routine reviews, model‑based portfolio construction and automated communications is increasingly automatable, while hybrid models that pair AI with human oversight preserve trust and handle complex life events.

The practical response is not wholesale redundancy but role redesign: move routine work to AI, keep humans for governance, personalised planning and relationship‑driven decisions, and insist on human‑in‑the‑loop design for high‑trust flows to meet compliance and fairness requirements.

For a snapshot of how GenAI enables hyper‑personalisation see the piece on how generative AI enables hyper‑personalized robo advisors, for how AI is reshaping wealth management and hybrid advisory models see AI‑driven wealth management and hybrid advisory models, and for why keeping people in critical decision loops matters see Nucamp's note on Nucamp AI Essentials: human‑in‑the‑loop design for high‑trust financial services; the upshot: advisers who learn to compose AI outputs, test for fairness and translate algorithmic recommendations into client stories will be the ones clients choose when life gets complicated, not just when markets move.

Practical next steps for Australian finance workers are straightforward: start with a quick skills audit, pick one low‑risk use case (an automated reconciliation or a single report) and run a controlled pilot, then scale with strong data governance and human‑in‑the‑loop checkpoints so you keep oversight where regulators and customers demand it; resources like Vena's practical guide on AI adoption highlight why starting small and proving value builds trust, and the LSBF primer on future finance jobs shows which technical and soft skills to prioritise - data literacy, promptcraft and explainability.

For individuals wanting career‑proof routes, short, job‑focused training works best: Nucamp's AI Essentials for Work is a 15‑week programme that teaches workplace AI tools, prompt writing and practical job-based skills (early bird $3,582; register at the AI Essentials for Work registration) so routine tasks can be handed to AI while human judgement, client storytelling and governance become the premium skills employers pay for; the goal is a practical pivot, not panic - move from processing to advising, one pilot at a time.

“Whether you actively adopt AI or not, you're likely already seeing it show up in your Excel models and in the tools you use every day. See it as an opportunity to learn more and build trust in these systems.” - John Colbert, VP of Advisory Services, BPM Partners