Australians under pressure from rising living costs are quietly handing more of their money-management tasks to algorithms — not to replace advisers, but to supplement them — and the shift is already reshaping how everyday people budget, file taxes and shop financial products.
A recent media report summarised new survey findings that show a sharp uptick in Australians using artificial intelligence for personal finance tasks such as budgeting, saving, tax preparation and product comparison. The story noted that roughly two in five Australians now report using AI tools for money management, with younger and cost‑squeezed households most likely to turn to free or low‑cost AI options rather than paying for professional advice. That dynamic — affordability driving tool adoption — is the single clearest thread running through these findings.
At the same time, industry and market analysis suggests this consumer behaviour sits inside a far larger structural shift: banks, fintechs and data vendors are embedding AI into credit decisioning, fraud detection, accounting automation and conversational interfaces. Independent forecasts and consultancy reports estimate that generative and automation‑driven AI could channel billions into Australia’s professional and financial services sector by 2030 — but not without important governance, accuracy and privacy challenges that policymakers and firms are only beginning to confront.
This feature unpacks what the consumer data shows, how incumbents and ASX small caps are responding, the practical strengths and risks of relying on AI for money matters, and what everyday Australians should know before letting an algorithm steer their wallet.
At the same time, analysts and alternative surveys present numbers that differ by definition and timing:
Why that matters now: professional financial advice often carries substantial upfront or recurring fees. When household budgets are tight, consumers will naturally seek lower‑cost alternatives that reduce friction even if they do not fully replace regulated, personalised advice.
For firms, the winning formula looks like machine efficiency + human judgement + strong governance. Those who can operationalise that triad while preserving transparency and consumer protection will capture the long‑term value of AI in finance.
AI is rapidly changing the practical economics of personal finance: cheaper advice‑adjacent tools, faster decisioning at scale and richer consumer interfaces. The critical next phase is not whether Australians will use AI for money — that is already happening — but whether the industry, policymakers and consumers can build trust, guardrails and literacy fast enough to ensure the benefits outweigh the risks.
Source: The Australian https://www.theaustralian.com.au/business/stockhead/content/do-you-trust-ai-with-your-money-millions-of-australians-already-do/news-story/b8c1d0fe1cdc576bd4cd852913ed5086/?amp=
A recent media report summarised new survey findings that show a sharp uptick in Australians using artificial intelligence for personal finance tasks such as budgeting, saving, tax preparation and product comparison. The story noted that roughly two in five Australians now report using AI tools for money management, with younger and cost‑squeezed households most likely to turn to free or low‑cost AI options rather than paying for professional advice. That dynamic — affordability driving tool adoption — is the single clearest thread running through these findings. At the same time, industry and market analysis suggests this consumer behaviour sits inside a far larger structural shift: banks, fintechs and data vendors are embedding AI into credit decisioning, fraud detection, accounting automation and conversational interfaces. Independent forecasts and consultancy reports estimate that generative and automation‑driven AI could channel billions into Australia’s professional and financial services sector by 2030 — but not without important governance, accuracy and privacy challenges that policymakers and firms are only beginning to confront.
This feature unpacks what the consumer data shows, how incumbents and ASX small caps are responding, the practical strengths and risks of relying on AI for money matters, and what everyday Australians should know before letting an algorithm steer their wallet.
What the surveys say — a clear trend, some mixed numbers
Compare Club’s research and contemporaneous coverage are the immediate catalyst for the recent headlines. Its public write‑ups (the company surveyed ~1,000 Australians in prior releases) documented growing experimentation with AI across budgeting, investing, tax and product comparison tasks — and that ChatGPT currently dominates the consumer landscape as the preferred assistant for general finance prompts. Compare Club’s reporting from 2024 found substantial interest and trial use among younger cohorts and higher‑income respondents, with significant willingness to adopt AI for budgeting in particular.At the same time, analysts and alternative surveys present numbers that differ by definition and timing:
- Chatbot and referral tracking consistently shows ChatGPT commanding the lion’s share of chatbot referrals (around 79–80% in StatCounter reporting), which helps explain why ChatGPT is the default “financial co‑pilot” for many consumers. Microsoft Copilot and Google Gemini trail by a wide margin in these referral metrics.
- Other consumer studies — from banks, consultancy groups and trade bodies — report variable usage rates for AI in personal finance depending on question phrasing (e.g., “have used AI ever?” versus “use it regularly?”) and sample frames. Some studies report lower baseline usage (mid‑teens to mid‑twenties percentage points), while others show rapid growth year‑on‑year as more people try generative assistants.
Where AI is already being used (and why it helps)
Consumers and households report leaning on AI most in these areas:- Budgeting help and tracking (the most common use case).
- Saving and basic investment idea generation.
- Comparing financial products (credit cards, loans, insurance).
- Plain‑language decoding of complex terms and tax concepts.
- Drafting or organising tax returns (as a preparatory aid, not certified tax advice).
- Fraud and scam detection signals (spotting suspicious payment or account activity).
Why that matters now: professional financial advice often carries substantial upfront or recurring fees. When household budgets are tight, consumers will naturally seek lower‑cost alternatives that reduce friction even if they do not fully replace regulated, personalised advice.
Trust, the “co‑pilot” model and the remaining gap with human advisers
The survey narrative is nuanced: Australians are warming to AI as a convenience tool, but they are not yet ready to treat it as a full substitute for human expertise. Key observations:- Professional financial advisers and government agencies remain among the most trusted sources for complex financial guidance; AI tools frequently score lower on trust metrics relative to advisers, but higher than social media and peer recommendations in some surveys. Consumers report fewer self‑reported errors from AI than from friends, social channels or influencers — but that does not mean AI is risk‑free.
- The emerging consensus among advisers and regulators is a hybrid model: AI as a financial co‑pilot, streamlining research and routine tasks, combined with human judgement for strategy, regulatory nuance and fiduciary responsibility.
- Trust is fragile and context‑dependent: consumers trust AI more for general explainers and comparison tasks, less for legally or tax sensitive actions. That’s consistent with advice from specialists who warn that AI outputs are only as reliable as their data inputs and the user’s verification habits.
Market and infrastructure signals: who’s building the plumbing
The consumer shift is mirrored by corporate moves across the ASX and global vendors that supply the plumbing for smarter financial services.- MoneyMe (ASX: MME) — a digital lender — has leaned heavily on data‑driven credit decisioning, allowing many small personal loan approvals to be processed in minutes via automated decision engines and machine‑assisted identity checks. The company markets fast digital approvals and has publicly said its credit models and fraud detection use automation to accelerate throughput. This is a concrete example of AI/ML replacing time‑intensive manual underwriting components.
- Reckon (ASX: RKN) — a provider of accounting and tax software for households and SMEs — has integrated automation across its accounting stack. The vendor and industry commentary make clear that automation in bookkeeping, bank reconciliation and compliance workflows is a priority. Reckon argues automation enables advisers and accountants to focus on higher‑value advisory tasks rather than repetitive data entry.
- Appen (ASX: APX) — a long‑standing data‑annotation and training‑data supplier — is a critical infrastructure player for model builders. High‑quality labelled data remains a cornerstone of accurate LLMs and domain models; Appen’s platform offerings and data services are widely used to prepare the ground truth that drives supervised and reinforcement learning pipelines. Appen’s business shows how upstream data vendors profit from the consumer‑facing AI boom.
- Unith (ASX: UNT) — a smaller ASX company focused on conversational AI and digital human platforms — shows how the interface layer is evolving. Unith’s digital‑human agents and conversational builders allow organisations to deploy human‑like assistants for customer support, product education and simple financial Q&A sessions at scale. These interfaces are what many consumers encounter when they “ask an AI” about their money.
The economic upside and the consulting forecasts
Independent modelling and industry collaborations agree on one thing: AI adoption will create sizeable value for Australia’s economy, particularly in professional and financial services. Estimates vary, but a widely‑cited analysis suggests generative AI could contribute a multi‑billion‑dollar annual uplift to professional and financial services by 2030 if responsibly deployed. Deloitte and industry forecasts highlight both productivity gains and significant disruption across jobs and business models. That upside is conditional: regulatory clarity, data sovereignty, workforce reskilling and robust governance matter enormously for whether the benefits are widely captured.Risks and failure modes — what can go wrong
There are several concrete hazards when people trust AI for financial decisions without proper safeguards:- Hallucinations and factual errors: generative systems sometimes invent plausible‑sounding but false statements (fabricated figures, misattributed product terms, jurisdictionally incorrect tax guidance). For finance, that can translate into improper trades, misclaimed deductions or poor product choices.
- Data currency and local rules: some AI outputs are trained on global datasets and can miss jurisdiction‑specific regulations or changes in tax law. Outputs that don’t respect local rules can mislead users in Australia if the model mixes international norms.
- Privacy and data exposure: feeding personal financial documents, bank statements or tax details into public chat interfaces can expose sensitive data that may be logged or used for model improvement unless the tool explicitly prohibits that use or offers a secure, enterprise‑grade workspace.
- Fraud and scam amplification: malicious actors can use generative tools to craft highly convincing scam messages, phishing pages or “deceptive deals.” That raises the bar for consumer vigilance.
- Overconfidence and automation bias: Users often overweight algorithmic suggestions, assuming accuracy. This can amplify small model biases into poor financial outcomes at scale.
Practical checklist for consumers using AI for money management
For readers curious to keep AI in their toolkit without over‑relying on it, adopt the following disciplined approach:- Verify core facts: always cross‑check AI outputs against primary documents (bank statements, official product PDS, ATO rulings for tax).
- Use secure, privacy‑conscious tools: prefer apps that offer dedicated, encrypted workspaces or enterprise-grade privacy settings for tax or identity data.
- Treat AI as a research assistant: use it to generate lists, plain‑English summaries, or scenario outlines — not to execute final tax filings or high‑value trades without professional sign‑off.
- Keep a provenance habit: ask the AI for sources and dates; if sources are missing or vague, treat the recommendation as tentative.
- Keep human expertise in the loop for major decisions: for investment strategies, estate planning, complex tax matters or debt restructuring, consult a licensed adviser.
- Be scam‑savvy: never respond to or act on requests for funds or login details that arrive via unsolicited messages, even if they appear personalised by AI.
Corporate practice: what “responsible AI” looks like in finance
Firms that want to embed AI into consumer financial services should take these operational steps:- Data governance: define what customer data can be used to train models, implement strict access controls, and log all model queries that touch sensitive data.
- Model lifecycle management: version models, maintain test suites, monitor drift and implement mechanisms for model rollback when performance degrades.
- Explainability and user consent: present outputs with clear confidence levels and provenance statements, and obtain explicit consent before using personal data for model improvement.
- Red‑team testing and hallucination audits: actively test models for fabricated claims, numeric accuracy and bias.
- Regulated handoffs: build workflows that escalate to human advisers for anything that falls under regulated advice or where model confidence is low.
Reality check: “robo‑advisers manage hundreds of billions” — a qualified correction
Media coverage often frames robo‑advisers as already managing “hundreds of billions” globally. The reality is more nuanced. Major US platforms like Betterment and Wealthfront have indeed scaled into the tens of billions of dollars of AUM individually (Betterment’s reported AUM sits in the tens of billions; Wealthfront likewise manages tens of billions), and combined digital asset platforms account for a very large market share of digitally managed assets — but claiming hundreds of billions for a single category can overstate the case unless the timeframe and included firms are clearly specified. Put simply: robo‑advisers are substantial and growing, but verify combined AUM sums before repeating broad round numbers.What regulators and consumer groups are watching
Policymakers in Australia and globally are focused on three related priorities:- Consumer protection and liability: clarifying who is responsible when an AI‑driven recommendation leads to a loss.
- Data protection and cross‑border data flows: ensuring personal financial data is handled to national privacy standards and not inadvertently exposed in model training pipelines.
- Financial advice regulation: defining what counts as “advice” when an algorithm produces a tailored recommendation, and whether and how existing licensing frameworks apply.
Investors & market watchers — where value and risk meet
For investors or market observers, the current landscape suggests several actionable themes:- Infrastructure winners: firms supplying training data, annotation services and model‑ops tooling (the “plumbing”) are positioned to benefit from broad demand for high‑quality data and model lifecycle services. Appen is a clear example of that supply layer.
- Platform adoption in incumbent services: traditional fintechs and software vendors integrating AI into workflows (loan decisioning, accounting automation, tax triage) may capture incremental margin improvements from automation and new product features. MoneyMe’s credit decisioning and Reckon’s automation work illustrate this trend.
- Experience and trust‑based plays: companies that can credibly marry AI speed with human oversight, transparent explainability and strong data governance will earn consumer trust and regulatory flexibility — a potential moat. Unith’s digital human deployments highlight the product opportunity at the consumer interface layer, though execution risk is material for small players.
Final analysis — what’s likely to happen next
- Short term (12–24 months): more households will use AI for low‑stakes tasks (budgeting, product comparison, plain‑English explainers). Fintechs and software vendors will accelerate embedding automation to reduce costs and improve speed — but large‑scale adviser displacement is unlikely in the near term. Many deployments will be hybrid: automation plus human oversight.
- Medium term (3–5 years): enterprise adoption and onshore model governance will accelerate. Firms that can provide secure, auditable consumer AI experiences will capture a premium. Sectoral value capture is plausible — forecasts indicate multi‑billion outcomes are feasible for financial and professional services if adoption is managed responsibly.
- Wildcards: regulatory shifts, major model failures or high‑profile privacy breaches could slow consumer adoption and raise compliance costs, altering the winners and losers.
Practical bottom line for readers
AI is no longer an experimental novelty in household finance — it’s a pragmatic tool millions of Australians are using to lower friction, learn faster, and save time. That shift is being driven by affordability and convenience, and it dovetails with a broader industry push to automate routine financial workflows. The most successful approach for consumers is a calibrated one: use AI to research, compare and simplify, but keep human experts and primary documents in the loop for decisions that carry material financial, tax or legal consequences.For firms, the winning formula looks like machine efficiency + human judgement + strong governance. Those who can operationalise that triad while preserving transparency and consumer protection will capture the long‑term value of AI in finance.
AI is rapidly changing the practical economics of personal finance: cheaper advice‑adjacent tools, faster decisioning at scale and richer consumer interfaces. The critical next phase is not whether Australians will use AI for money — that is already happening — but whether the industry, policymakers and consumers can build trust, guardrails and literacy fast enough to ensure the benefits outweigh the risks.
Source: The Australian https://www.theaustralian.com.au/business/stockhead/content/do-you-trust-ai-with-your-money-millions-of-australians-already-do/news-story/b8c1d0fe1cdc576bd4cd852913ed5086/?amp=