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‘Computer says no’: I asked AI the pitfalls of asking AI for money advice

Read the original at The Sydney Morning Herald ↗
Cite

Pedersen-McKinnon, N. (2026, September 25). ‘Computer says no’: I asked AI the pitfalls of asking AI for money advice. The Sydney Morning Herald. https://www.smh.com.au/money/planning-and-budgeting/computer-says-no-i-asked-ai-the-pitfalls-of-asking-ai-for-money-advice-20260926-p610k6.html?ref=rss&utm_medium=rss&utm_source=rss_feed

Dated when The Aporia collected the article, usually within hours of publication. Check the original for its exact date and byline.

The Sydney Morning Herald · collected 2026-09-25 · by Nicole Pedersen-McKinnon

Quick Summary

The article discusses an experiment where the author queried artificial intelligence about the pitfalls of using AI for financial advice. According to a recent Understanding Modern Australia report, over three million Australians use AI for managing their finances, with Gen Z leading at 25%. However, only 20% trust AI to recommend financial products tailored to individual needs. The AI responses highlight major issues such as lack of accountability, regulatory concerns, insufficient personalization, and challenges in keeping currency and accuracy up-to-date.
Written by AI on 2026-09-25, from this article's own text only (the story summary below draws on all the coverage).

AI analysis

Story summary

Nicole Pedersen-McKinnon from the Sydney Morning Herald explores the pitfalls of relying on artificial intelligence for financial advice. According to a recent report by life insurer TAL and research firm McCrindle, 3 million Australians are using AI to manage their finances, with 25% of Gen Z users leading the trend. Despite this usage, only 22% feel comfortable using AI for major financial decisions, and just 20% trust it to recommend suitable financial products.

When questioned about the reliability of AI advice, an AI language model (LLM) highlighted several issues: incorrect figures, invented products or rules that do not exist, and misapplying rules from one country's system (like US 401k rules) to Australia’s superannuation system. The LLM stressed these inaccuracies could occur while sounding very authoritative, underscoring the risks of blindly trusting AI financial advice.

Written for “AI Financial Advice Pitfalls” on 2026-11-02, grounded in this article and the 0 other(s) covering the same event.

Story

📰 AI Financial Advice Pitfalls
Technology · 1 article(s) covering the same event.

Compared with similar articles

This article reads unscored and hedges 17% of its claims. Each row says how that neighbour differs.
The Sydney Morning Herald
Hedges 7 of 44 claims Outlet record 61/100
“The articles discuss different aspects of AI and its impacts on society, with Article A focusing on personal financial advice and Article B on AI's influence on Wall Street and stock market volatility.”
How this article was measured signals, leaning charts, publisher record, byline, topics and claims

Signals How these are calculated →

Claims extracted
53
sentences that state something as happening or true
Uncertain
17%
9 of 53 hedged ("reportedly", "may", "according to")
Leaning
not political
takes no side on a contested political question
Publisher signals
61/100
The Sydney Morning Herald's corrections and hedging, across everything collected from it. Not a measure of accuracy.
Outlets on this story
1
Technology
Same framing elsewhere
1
articles that frame the event the way this one does
Analyzed 2026-09-25 · how these are computed

How this is being covered How these are calculated →

Article leaning vs. publisher reliability
Source leaning vs. consistency

Publisher

The Sydney Morning Herald · 5845 article(s) · 15 correction(s) detected
Running correction rate · 15 correction(s)

Who wrote this

Nicole Pedersen-McKinnon
3 article(s) here · 1 carrying a prediction
🔮 There’s a certain tone, and maybe head tilt, friends give me when they want to talk about money.
🔮 All of our biggest lenders predict that on Tuesday there will be another interest rate increase.
🔮 In an emphatic “computer-says-no”, here are the 10 reasons the AI gave me not to use it – and how I might.
Also by Nicole Pedersen-McKinnon
The simple money advice I always give my closest friends
2026-10-02 · The Sydney Morning Herald
Nothing else under this byline is closely related to this article, so these are simply their most recent.

Topics

Australia Australians LLM McCrindle TAL

Subjects

LLM ORG · 4× Australians NORP · 2× Claude ORG · 2× Australia GPE · 1× Australian NORP · 1× McCrindle ORG · 1× TAL ORG · 1×

Narrative

How’s this for candour: “LLMs can state incorrect figures, invent products or rules that don’t exist, or misapply a rule from one country’s system (e.g., US 401k rules) to Australia’s superannuation system, all while sounding authoritative.”
framing: assertive · carried by 1 article(s) · first seen 2026-09-25
🔮 In an emphatic “computer-says-no”, here are the 10 reasons the AI gave me not to use it – and how I might.
2026-09-25 · The Sydney Morning Herald
‘Computer says no’: I asked AI the pitfalls of asking AI for money advice · assertive framing

Claims (53 extracted, 9 hedged)

Managing money well can be, well, confusing. asserted
Managing → manage → money
And how are many of us are solving such issues these days with the help of artificial intelligence “buddies”. asserted
many → solve → buddies
Heck, I even have friends who use AI to draft replies to tricky text messages from their mum. asserted
who → have → mum
So is it any surprise that more than three million Australians are already using AI to help manage their financial activity, with the highest number – 25 per cent – among Gen Z Australians, says a new Understanding Modern Australia report. asserted
report → use → Australians
Not that we necessarily – thank goodness – trust it. asserted
we → thank → it
The report, by life insurer TAL with McCrindle, found only 22 per cent of us feel comfortable using AI to help make major financial decisions and only 20 per cent trust AI to recommend a financial product suited to their needs. asserted
cent → find → needs
So, I thought I’d try an experiment: I asked AI the pitfalls of asking AI for money advice – and whoa. asserted
I → think → advice
In an emphatic “computer-says-no”, here are the 10 reasons the AI gave me not to use it – and how I might. uncertain
I → say → it
(I’ve grouped them where AI doubled and even tripled down.) asserted
AI → group → them
‘An LLM’s training data has a cut-off, and even with web search, it can miss nuance, fail to update thresholds, blend outdated and current rules without flagging the difference. asserted
it → have → difference
No care and no responsibility does not make for the ideal economic confidante. asserted
care → make → confidante
The rules and requirements to give financial advice in Australia are strict. asserted
rules → give → Australia
AI is not bound by any of them. asserted
AI → bind → them
The education needed is also now extensive (part of why the profession is shrinking). asserted
profession → need → ?
But, mainly, a licensed financial adviser is bound by law to act in your best interest. asserted
adviser → bind → interest
In the candid confession, AI said: “An LLM has no such obligation and no licence that can be revoked if it gets things wrong.” asserted
things → say → obligation
2. Regulatory grey zone You need an Australian Financial Services Licence to give personal financial advice – it’s illegal otherwise. asserted
it → need → advice
Reputable AI tools build in disclaimers and stick to general information rather than personal advice. asserted
tools → build → advice
But how do you even tell the difference when it’s – you know – you asking? 3, 4 and 5. asserted
you → tell → difference
Generic, one-size-fits-all framing. asserted
size → fit → all
Variables that factor into smart financial decisions include your age, income, debts, risk tolerance, tax situation, existing assets, super balance, insurance and goals. asserted
that → factor → age
Does your AI know all of this about you? asserted
AI → know → you
And, crucially, do you want it to? asserted
it → want → ?
Where does that data go and how is it stored? asserted
it → go → ?
“Even if they did, an LLM isn’t verifying any of it or cross-checking for inconsistencies the way a human adviser would,” confides the LLM itself. asserted
LLM → do → inconsistencies
The advice might just be versions of old financial adages like diversify, pay off debt and invest for the long term. uncertain
advice → diversify → term
They may not fit with your financial affairs at that moment. uncertain
They → fit → moment
Legislation changes regularly. asserted
Legislation → change → ?
Even in the past year, we have seen changes to the first home buyer 5 per cent deposit scheme, an overhaul of capital gains and a ban on negative gearing. asserted
we → see → gearing
Um, I won’t be trusting AI with any related financial decisions thank you! asserted
I → trust → you
Old information could easily sink your future. uncertain
information → sink → future
“An LLM’s training data has a cut-off, and even with web search, it can miss nuance, fail to update thresholds, blend outdated and current rules without flagging the difference.” asserted
it → have → difference
A false sense of diligence and confident-sounding errors (aka “hallucinations”) We’ve all encountered this - AI intel that’s slightly insane! asserted
that → encounter → this
And the danger it poses in complex financial information or advice is: would you even recognise it? asserted
you → pose → it
AI can look well-reasoned and comprehensive, giving you a sense of security when you should have nothing of the sort. asserted
you → look → sort
How’s this for candour: “LLMs can state incorrect figures, invent products or rules that don’t exist, or misapply a rule from one country’s system (e.g., US 401k rules) to Australia’s superannuation system, all while sounding authoritative.” asserted
that → ’ → system
That you might be responding based on your childhood “conditioning” rather than actual conditions? uncertain
you → respond → conditioning
Or that you might have a different risk tolerance to the questions you are asking? uncertain
you → have → questions
An AI model can’t possibly do this. uncertain
model → do → this
Indeed: “An LLM responds to the words in the prompt, not the psychology behind them.” asserted
LLM → respond → them
…and 13 more, not listed.
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