More than a third of New Zealand retail investors now say they use generative AI tools such as ChatGPT and Microsoft Copilot to inform their investment decisions — and a large majority report being satisfied with the outcomes — a shift that is simultaneously pragmatic and precarious for markets, advisers and regulators. (rnz.co.nz)
The findings were reported after Chartered Accountants Australia & New Zealand (CA ANZ) released its annual investor confidence work, which surveyed retail investors across Australia and New Zealand and highlighted rising domestic confidence alongside growing use of AI in retail decision-making. The RNZ summary of the CA ANZ survey states that “more than a third” of New Zealand retail investors reported using AI tools to make investment decisions and that 76 percent of those users were satisfied with results. The RNZ report also noted that the CA ANZ study showed 79 percent of respondents had increasing confidence in New Zealand capital markets and listed companies — even while worries about political unrest and trade wars are growing. (rnz.co.nz)
CA ANZ’s public overview of the 2024 investor confidence project places the survey in context: it is a multi-year program that canvassed more than 1,500 retail investors across Australia and New Zealand, with country-level downloads and datasets available for deeper inspection. The organisation continues to emphasise the importance of audited financial information and the role of auditors in preserving market trust — themes that are now intersecting directly with the rise of AI-sourced investor intel. (charteredaccountantsanz.com)
At the same time, global investor research from other professional services firms shows a broader appetite among investors for AI to deliver tangible productivity and financial improvements. PwC’s Global Investor Survey, for example, finds that a large share of investors expect generative AI to raise productivity and that investors want firms to pair AI investment with workforce upskilling — a signal that institutional sentiment is converging on both opportunity and the need for governance. (pwc.com)
A recent Chartered Accountants Worldwide / Ipsos study further supports this view: the profession sees a growing role in governing and validating the information flows that feed AI systems, and younger accountants are already heavy users of AI in their workflows. These developments point to a future where audited datasets, model factsheets and independent assurance become mandatory components of any reliable AI-driven investment process. (charteredaccountantsanz.com)
Auditors and accountants have an opening — and arguably a duty — to step into the “data guardian” role by certifying the inputs that feed investor-facing AI systems. At the same time, platforms and tool vendors must improve provenance, transparency and explainability to convert fragile trust into durable confidence.
The practical path forward for investors and markets is straightforward: embrace AI as an augmentative tool, not as a final decision-maker; insist on verifiable sources and auditable trails; and use human judgment as the final arbiter for actions that materially affect wealth. When these guardrails are in place, AI can be a powerful productivity enhancer for investors — but without them, the technology risks amplifying errors and producing concentrated, fragile market behaviours. (rnz.co.nz, charteredaccountantsanz.com, pwc.com)
Source: RNZ Investors turn to AI to make decisions
Background
The findings were reported after Chartered Accountants Australia & New Zealand (CA ANZ) released its annual investor confidence work, which surveyed retail investors across Australia and New Zealand and highlighted rising domestic confidence alongside growing use of AI in retail decision-making. The RNZ summary of the CA ANZ survey states that “more than a third” of New Zealand retail investors reported using AI tools to make investment decisions and that 76 percent of those users were satisfied with results. The RNZ report also noted that the CA ANZ study showed 79 percent of respondents had increasing confidence in New Zealand capital markets and listed companies — even while worries about political unrest and trade wars are growing. (rnz.co.nz)CA ANZ’s public overview of the 2024 investor confidence project places the survey in context: it is a multi-year program that canvassed more than 1,500 retail investors across Australia and New Zealand, with country-level downloads and datasets available for deeper inspection. The organisation continues to emphasise the importance of audited financial information and the role of auditors in preserving market trust — themes that are now intersecting directly with the rise of AI-sourced investor intel. (charteredaccountantsanz.com)
At the same time, global investor research from other professional services firms shows a broader appetite among investors for AI to deliver tangible productivity and financial improvements. PwC’s Global Investor Survey, for example, finds that a large share of investors expect generative AI to raise productivity and that investors want firms to pair AI investment with workforce upskilling — a signal that institutional sentiment is converging on both opportunity and the need for governance. (pwc.com)
What the CA ANZ survey and RNZ reporting actually say
Key numeric takeaways (as reported)
- More than one-third of New Zealand retail investors reported using AI tools such as ChatGPT and Microsoft Copilot for investment decisions; 76 percent of those users said they were satisfied with the results. (rnz.co.nz)
- The CA ANZ work reported 79 percent of respondents had increasing confidence in New Zealand capital markets and listed companies; confidence in capital markets rose by 6 percentage points year-on-year, while confidence in overseas markets reportedly fell by 5 percentage points. (rnz.co.nz)
- Younger investors are leading AI adoption: nearly two-thirds (64 percent) of respondents aged 18–29 were using AI in their investment decision-making. Geographic differences were reported too — Auckland had the highest reported AI usage (51 percent), followed by Canterbury (33 percent) and Wellington (27 percent). (rnz.co.nz)
- Trust in audited financial statements remained high: 88 percent of New Zealand investors reportedly expressed confidence in audited financial statements, and auditors were ranked the most trusted group to advance investor protection and market integrity. (rnz.co.nz, charteredaccountantsanz.com)
Caveats on interpretation
- The headline AI-adoption numbers come via RNZ’s reporting of CA ANZ results. The CA ANZ country page confirms the study’s existence and provides downloads for the New Zealand-specific report, but the RNZ article is the immediate source for the detailed breakdown cited above. Readers should consult the CA ANZ New Zealand report to verify sample sizes, question phrasing and exact breakdowns before making firm claims based on marginal percentage-point changes. (rnz.co.nz, charteredaccountantsanz.com)
- Survey definitions matter: “use” of AI can range from occasional prompts to ChatGPT for a quick data check, through regular Copilot-driven portfolio analysis, to integrated robo-advisers and agentic systems that can take transactional actions. The distinction between light trial use and operational reliance is material but not always made explicit in headline summaries; independent surveys of investor attitudes to AI show trust and acceptance vary substantially with familiarity and described use-cases. (pwc.com, rnz.co.nz)
Why investors are turning to AI — practical drivers
AI is attractive to retail investors for several practical reasons:- Speed of research: generative models and copilot tools can summarise earnings transcripts, pull together newsflow and produce comparative line-item summaries far faster than manual searches. This can shorten the research cycle from hours to minutes.
- Accessibility of analysis: retail investors can prompt models for valuations, scenario modelling and plain-language explanations of financial statements, lowering the barrier to building a hypothesis-driven investment case.
- Behavioural factors: younger investors are more digitally native and more comfortable iterating with algorithmic assistants; the data in the CA ANZ reporting point to significantly higher AI uptake in the 18–29 cohort. (rnz.co.nz)
- Cost: compared with paid analyst research or advisory fees, many AI tools offer low-friction, low-cost alternatives that satisfy casual or self-directed investors’ needs.
Strengths and immediate benefits
- Democratization of basic analysis: AI lowers the time and skills barrier for everyday tasks such as screening stocks, summarising company announcements and comparing metrics across peers.
- Enhanced information synthesis: models can combine structured data (financials) with unstructured data (news, transcripts) to produce narrative explanations or risk lists that can be useful starting points for deeper diligence.
- Faster idea validation: investors can use AI to quickly surface counterarguments, complementary evidence and historical analogues — speeding iterative research and allowing retail investors to manage more ideas with less effort.
- Scale and personalization: Copilot-style integrations in productivity apps can deliver tailored dashboards, watchlists and alerts that reflect individual preferences and risk tolerances.
Material risks and limitations
While the immediate benefits are compelling, the adoption of AI by retail investors — and by the broader investor ecosystem — introduces specific risks that require attention.1. Garbage in, garbage out: data quality and model training
AI advice is only as good as the data and models that underpin it. If models are trained on inaccurate, stale or biased financial data, they will amplify those errors. CA ANZ and market commentators emphasise the continuing primacy of audited financial statements as the reliable source layer beneath any AI-driven analysis. Retail investors relying on unsourced AI outputs risk making decisions on partial or incorrect information. (rnz.co.nz, charteredaccountantsanz.com)2. Hallucinations and unverifiable outputs
Generative models can produce confident-sounding but incorrect assertions (hallucinations). For an investor, a plausible but false narrative (e.g., an invented partnership, misstated revenue figure or wrongly attributed quote) can be costly if acted upon without verification.3. Overconfidence and fragile trust
The CA ANZ analysis highlighted that although many investors use AI, trust is fragile: significant proportions of non-users told the survey they do not trust AI outputs, and nearly half of non-users preferred other information sources. This bifurcation suggests that heavy reliance without human verification could create fragile behavioural regimes where investors accept false positives from AI until adverse outcomes force rapid re-pricing. (rnz.co.nz)4. Lack of audit trails and explainability
Many consumer-facing AI tools lack transparent provenance and auditable decision logs. This makes it difficult for an investor to demonstrate the basis of a decision, or for a regulator or adviser to reconstruct events after a loss or market disruption. Institutional-grade adoption demands immutable logs, model factsheets, and reproducible test cases — capabilities that are still maturing.5. Concentration and crowding risk
If a popular AI prompt or model produces similar trade ideas for a large cohort of retail users, the result can be crowded trades and amplified volatility in small-cap or illiquid securities. AI-driven consensus narratives can become self-reinforcing until a liquidity shock reverses them.6. Regulatory and ethical exposures
Automated or semi-automated investment advice that crosses into regulated financial advice territory raises legal and compliance issues. The regulatory environment for AI-driven financial advice is nascent in many jurisdictions; the boundaries between “information” and “advice” will be litigated and regulated in coming years.The auditor and accountant as “data guardian”: what it means
CA ANZ and broader chartered-accountant networks are reframing auditors and accountants as data guardians — the stewards of high-quality financial information that should underpin both human and machine decision-making. The CA ANZ commentary stresses that trust in audited financial statements remains robust (88 percent expressed confidence), and that audit will remain the bedrock of investor intelligence even as AI proliferates. (charteredaccountantsanz.com, rnz.co.nz)A recent Chartered Accountants Worldwide / Ipsos study further supports this view: the profession sees a growing role in governing and validating the information flows that feed AI systems, and younger accountants are already heavy users of AI in their workflows. These developments point to a future where audited datasets, model factsheets and independent assurance become mandatory components of any reliable AI-driven investment process. (charteredaccountantsanz.com)
Practical guidance for retail investors using AI
Retail investors who choose to use AI should adopt disciplined habits to limit downside and improve outcomes:- Verify critical facts. Cross-check any AI-sourced numerical claim (revenues, margins, contract terms) against primary filings or audited statements before acting.
- Use AI for scoping, not execution. Treat AI as a research assistant that surfaces questions, not as an automated trader that executes on its own. Maintain human oversight for any buy/sell decision.
- Demand provenance. Prefer tools that show source links, timestamps and model confidence scores for every output.
- Maintain record-keeping. Save prompts, model outputs and the data sources referenced as part of your decision log.
- Understand limitations. Know whether the AI uses cached data, real-time feeds, or proprietary datasets; adopt conservative position sizing if any output cannot be fully verified.
- Seek audited information. Lean on independent audited financial statements as the foundation for any financialmodeling or valuation work. CA ANZ’s survey shows investors still rank auditors highly in trustworthiness for market integrity. (charteredaccountantsanz.com, rnz.co.nz)
What firms, advisers and regulators should do next
- Insist on verifiable data pipelines: platforms and advisers should integrate authenticated data sources (e.g., exchange feeds, audited filings) and publish model factsheets that summarise training data, limitations and known failure modes. Independent providers and auditors can play a role validating those pipelines.
- Create minimum disclosure standards: if a firm or platform uses AI to produce investment recommendations, it should disclose the model class, data vintage, confidence levels and human review processes.
- Support AI literacy for retail investors: regulators, professional bodies and consumer groups should fund clear, accessible guides that explain strengths and hallucination risks of generative AI in financial contexts.
- Expand audit and assurance frameworks: as CA ANZ notes, audited financial statements remain central; auditors should develop assurance protocols and controls specific to AI-fed analytics and to the datasets used to train investment-grade models. (charteredaccountantsanz.com)
- Consider phased policy for automated execution: regulators might differentiate between “information-only” outputs and outputs that trigger trade execution or portfolio rebalancing, with progressively stricter rules and accountability for higher-impact automation.
How to read the signals: a synthesis
- The rise in AI usage among retail investors is real and meaningful — particularly among younger cohorts who treat AI as a routine research tool. The CA ANZ reporting, as presented in the RNZ coverage, captures this behavioural shift while simultaneously showing that core trust in audited financial statements and auditors remains strong. (rnz.co.nz, charteredaccountantsanz.com)
- AI is lowering the cost of generating investment ideas, but it is not yet a substitute for primary, audited information and human judgment. The immediate market-level risk is operational (hallucinations, crowded trades, provenance gaps), not a mysterious new financial black box. That makes remedial action — stronger data provenance, audit-level assurance, and clear disclosure — both possible and effective.
- Institutional and professional bodies are already converging on the “data guardian” concept: accountants and auditors will likely play a central role in certifying the inputs and outputs that feed AI investment workflows. That role will be pivotal for restoring and maintaining investor trust in an AI-enabled ecosystem. (charteredaccountantsanz.com)
A practical checklist for investors and market participants
- For individual investors:
- Verify all AI-generated numeric claims against audited filings.
- Keep conservative position sizes when acting on AI-sourced signals.
- Maintain a decision log (prompt → output → verification steps → action).
- Prefer platforms that expose data provenance and model explainability features.
- For advisory firms and fintechs:
- Build immutable audit trails for data, prompts and model outputs.
- Publish model factsheets and data lineage for any recommendation engine used externally.
- Incorporate human-in-the-loop approvals for high-impact decisions.
- Engage auditors to establish assurance over critical training and reference datasets.
- For regulators and standard-setters:
- Draft guidance that differentiates information tools from regulated financial advice.
- Require minimum provenance and disclosure standards for AI-driven recommendation services.
- Support upskilling programs and consumer education to reduce asymmetric comprehension.
Final assessment — opportunity with clear boundaries
The CA ANZ survey signals an important inflection: retail investors are increasingly comfortable using AI tools as part of their decision-making, and many report positive outcomes. That’s an important market-development: AI is democratizing access to synthesis and analysis that was once the purview of paid research desks. But that democratization does not eliminate the need for reliable, auditable source data or the safeguards that make markets resilient.Auditors and accountants have an opening — and arguably a duty — to step into the “data guardian” role by certifying the inputs that feed investor-facing AI systems. At the same time, platforms and tool vendors must improve provenance, transparency and explainability to convert fragile trust into durable confidence.
The practical path forward for investors and markets is straightforward: embrace AI as an augmentative tool, not as a final decision-maker; insist on verifiable sources and auditable trails; and use human judgment as the final arbiter for actions that materially affect wealth. When these guardrails are in place, AI can be a powerful productivity enhancer for investors — but without them, the technology risks amplifying errors and producing concentrated, fragile market behaviours. (rnz.co.nz, charteredaccountantsanz.com, pwc.com)
Quick reference actions
- If you use AI for investing: verify, document, and size conservatively.
- If you build AI tools for investors: publish provenance, implement audit trails, and design human-in-the-loop controls.
- If you regulate or audit markets: prioritise standards for data quality, transparency and assurance that explicitly cover AI-fed analytics.
Source: RNZ Investors turn to AI to make decisions
