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

New Zealand's financial services workforce is feeling AI's double effect: productivity surges and real disruption for routine roles. Recent surveys show AI use leapt to the mainstream - with major gains in efficiency - while the Reserve Bank's Financial Stability Report flags how AI can reshape risk and operations (see the Reserve Bank's “Rise of the machines” analysis).

The Government's new AI Strategy is pushing adoption and guidance for responsible use, changing the rules for banks, insurers and advisers alike; firms that automate OCR/NLP document processing or deploy 24/7 chatbots can cut processing time dramatically, but human oversight remains essential.

For Kiwis in tellering, claims processing and junior reporting roles, the practical fix is skills: short, work-focused courses - like Nucamp's AI Essentials for Work - teach usable AI tools and prompt-writing so teams stay valuable as AI handles the repetitive stuff.

“It is encouraging to see New Zealand organisations capitalising on the benefits AI offers. We are still seeing business leaders calling for greater guidance and support around AI...” - Justin Gray, Datacom

Selection followed a practical, NZ-first filter: priority was given to Christchurch- and Wellington-relevant evidence from national bodies and industry analysis, cross-checked for concrete automation signals (OCR/NLP document processing, 24/7 chatbots and agentic assistants), adoption metrics and policy context.

The shortlist drew heavily on the Reserve Bank's special topic, the Reserve Bank's Reserve Bank of New Zealand "Rise of the Machines" AI analysis (May 2025) and the Financial Markets Authority's field research - e.g., firms' current use and plans - captured in the Financial Markets Authority research on AI use in New Zealand financial services.

Roles were only included if multiple NZ sources flagged routine, repeatable tasks as susceptible to AI, if local adoption or consumer demand suggested real near-term exposure (67% of larger NZ businesses now use some form of AI while many SMEs remain hesitant), and if there was a clear pathway to adaptation through governance, training or tool changes; that mix - risk, evidence and realistic reskilling prospects - kept the list focused on jobs where disruption is likely and where Kiwi workers and employers can act now.

“We reviewed how AI is currently used in New Zealand financial services and firm's plans for future applications. We sought to understand both the benefits and the risks to inform more oversight.” - Financial Markets Authority media release

Retail bank tellers and branch operations staff in New Zealand are facing a clear, local squeeze: branch visits have fallen sharply (the trend shows a 47% drop since 2019) as banks push digital channels and introduce chatbots, document automation and predictive tools that absorb routine transactions and paperwork.

Banks across Australia and NZ are reporting real efficiency gains from these technologies - chatbots and NLP handle many repetitive queries and OCR/NLP speeds up back‑office processing - so the job now looks less like counting cash and more like helping customers use digital services or resolving the complex cases the machines can't.

Regulators and industry research stress the balance: the Reserve Bank's "Rise of the machines" analysis and the Financial Markets Authority flag both benefits and governance risks, while ANZ-focused industry reporting highlights faster, personalised service alongside legacy-system and inclusion challenges.

The practical response for tellers and employers is to pivot: learn to support digitally vulnerable customers, move into advisory or exception‑handling roles, and adopt documented AI controls - supported by targeted short courses and on‑the‑job training that teach OCR/NLP and chatbot oversight.

“There is still considerable uncertainty around how AI will shape the financial system.” - Kerry Watt

Call-centre customer service and helpdesk agents in New Zealand's banks and insurers are already seeing the routine stuff disappear to chatbots, agent-assist tools and intelligent document processing - Fintrade documents how insurers now use OCR/NLP and machine learning across underwriting, claims and service - so agents spend less time repeating the same checklist and more time on the complex cases that actually need human judgement.

The lift in speed and straight‑through processing can be dramatic (some vendors report big reductions in back‑office time), and international contact‑centre studies show AI can cut manual work and even reduce processing costs by a quarter, but Kiwi customers remain cautious: a New Zealand survey found 67% expect lower fees and 57% faster transactions from AI while only 45% fully trust AI agents.

That mix makes the local playbook clear - use AI to contain and automate predictable queries, upskill agents into adviser roles with real‑time AI copilots, and pair deployments with the governance and transparency the FMA is pushing so customers and regulators can see how decisions are made; the result should be a quieter, more purposeful contact floor where staff solve the 1% of cases that matter most.

“Today's financial services contact centers are strategic hubs that foster trust, loyalty, and growth,” - Richard Winston, Global Financial Services Lead, Slalom

Junior financial analysts and routine reporting roles in New Zealand are being reshaped fast: AI now lets juniors run comprehensive market research and produce forecasts in hours rather than days, so entry-level work that once taught the basics is being automated and the bar for hireable skills has risen - as explained in Breaking Through the AI Hype: The Real Opportunity for Junior Talent, where productivity in AI-exposed industries jumped from 7% to 27%.

That's both risk and opportunity: task-heavy reporting, reconciliation and routine modelling are especially exposed (international reporting and local analysis flag automated financial analysis as a pressure point), yet organisations that train models on company data and use AI for personalised onboarding can compress learning time dramatically and turn new hires into productive analysts much sooner.

The Treasury's economic note also warns New Zealand's slow diffusion of technology could limit these gains unless employers pair adoption with reskilling and governance, so the practical play for Kiwi juniors is clear - master AI tools, learn to validate outputs and tell the story behind the numbers, because judgment and domain knowledge are the features that will keep analysts in demand when the spreadsheets write themselves.

"AI is rewriting the rules of work in New Zealand, and it's happening faster than most realise. Think of it as the internet's arrival in the '90s, but on steroids - it's not just a tool; it's a revolution that's reshaping jobs, industries, and how we live." - Christopher Walsh, MoneyHub

Basic bookkeepers, accounts clerks and back‑office teams in New Zealand are squarely in AI's sights because the repetitive stuff they do day‑to‑day - receipts, invoice capture, bank feeds and routine reconciliation - is now handled by smart tools that learn as they go; Xero, the New Zealand‑built platform, bundles features like Hubdoc data extraction, automated bank reconciliation and the JAX conversational assistant that cut manual coding and speed forecasting, and Xero's own Xero AI guide for accountants and bookkeepers shows how those features shift work from data entry to review and advice.

Add third‑party robotic bookkeepers that plug into Xero and promise round‑the‑clock reconciliation - for example Booke AI Xero integration for automated categorisation and matching automates categorisation and matching and advertises dramatic time savings - and the practical picture is clear: routine processing roles face real displacement, but there's an equally clear pathway to adapt by mastering exception‑handling, quality control, AI oversight and client‑facing advisory skills so the team interprets and vets machine outputs rather than keying them in by hand.

Junior underwriters and loan‑origination processors in New Zealand are on the front line of AI change: machine‑learning models now pull in behavioural signals, geographic and economic data to produce finer risk scores, while intelligent document processing (OCR/NLP) chews through paperwork that once drove whole shifts - so the role is shifting from data entry to checking and contesting machine outputs.

That means routine risk‑modelling and compliance checks are increasingly automated, but not foolproof: experts warn of incomplete information, opaque correlations and accountability questions unless firms pair systems with strict oversight.

The practical Kiwi response is twofold - build hands‑on skills in model validation, exception handling and audit trails, and adopt industry governance frameworks that scale with risk - both to protect customers and to keep underwriters valuable as decision‑support specialists rather than button‑press operators.

Read the InsuranceBusiness coverage of FSCL's findings on underwriting and the Government debate on regulation, and see AMS's practical walkthrough of underwriting risks and opportunities for how teams can adapt.

“There are a number of examples we could draw on. The most discussed one is the EU AI Act, which employs a risk framework similar to what we are proposing in our letter. I think that is a good starting point, alongside what has been done in Australia.” - Dr Andrew Lensen

Practical steps for Kiwis and employers stack up simply: start by building AI literacy across teams using Aotearoa-focused resources like Ako Aotearoa's Beginner's guide to AI literacy for educators to learn the basics, ethical checks and the SAIL framework for scaffolded capability, then follow with short, hands‑on training (for example UC Online's 20‑hour AI short course or Canterbury's two‑day Generative AI Bootcamp) so staff can practise prompting, evaluate outputs and manage exceptions; for workplace-ready, role‑specific reskilling consider a structured programme such as Nucamp's Nucamp AI Essentials for Work bootcamp (15 weeks) or its syllabus at Nucamp AI Essentials for Work syllabus.

Pair learning with small pilot projects (clear success metrics, human review checkpoints and basic audit trails), embed simple governance (who verifies model outputs, data privacy checks) and create career pathways that move people from data‑entry tasks into exception‑handling, advisory and model‑validation roles; the combination of short courses, local frameworks and practical pilots turns disruption into an upskilling opportunity rather than a cliff edge.

“I asked ChatGPT to turn my lesson plan bullet points into a first draft - it saved me 30 minutes and gave me a new idea for an activity.” - Foundation Tutor, NZ