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The Australian Securities and Investments Commission (ASIC) has laid down strict parameters regarding how brokers harness automated decision engines and artificial intelligence (AI) tools in its newly released report, Mortgage brokers: Review of best interests duties and complaints handling practices.
As well as reviewing how brokers and aggregators are meeting their best interests duty (BID) requirements, the financial regulator has also reviewed how brokers are using AI and what it expects from aggregators when it comes to oversight of the technology.
The review evaluated software tools, filtering engines, and oversight frameworks across 13 credit licensees representing eight major aggregator groups, which collectively encompass 66 per cent of Australia’s mortgage broker network.
The eight aggregator groups evaluated in the review were:
- Australian Finance Group (AFG)
- Connective
- Finsure Finance
- Lendi Group
- Loan Market Group (LMG)
- Mortgage Choice
- outsource Financial
- Yellow Brick Road.
Modernising systems across the channel
Industry technology is advancing beyond basic loan comparison software towards sophisticated digital workflows. In Report 000, ASIC observed that many major credit licensees are undergoing significant platform overhauls to address historic compliance gaps and streamline credit assistance.
Technology development in broking is currently concentrating on four key operational areas:
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Core processing automation: Automating back-office tasks, including consumer information collection, identity verification, borrowing capacity calculations, and loan application drafting.
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Tech-enabled fraud detection and auditing: Utilising data analytics and machine learning to catch tech-enabled fraudulent application data, identify file discrepancies, and assist quality assurance teams with real-time file audits.
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Lender policy matching engines: Deploying automated engines that parse complex lender credit policies to quickly determine borrower product eligibility.
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Savings and product selection tools: Exploring tools designed to systematically identify cheaper loan options and potential interest savings for consumers over the life of a loan.
ASIC welcomed these technological uplift programs, acknowledging that AI-assisted policy engines and analytics can help brokers work faster while accurately identifying products that suit a consumer’s specific circumstances.
Governance and data quality risks
Despite the clear operational benefits, the regulator warned that digital innovation creates serious compliance vulnerabilities if technology adoption outpaces internal risk management.
A central issue identified during file reviews involved data quality and automated software shortcuts. Aggregator comparison platforms frequently failed to capture temporary lender discounts, promotional cashbacks, or ad-hoc fee waivers without manual broker intervention. When software relies on incomplete data feeds, automated decision engines risk generating inaccurate total loan cost rankings.
Furthermore, ASIC cautioned against software features that allow brokers to insert auto-populated, generic rationale into client files. Automated boilerplate notes fail to demonstrate that a broker considered a client’s individual priorities, particularly when recommending a loan that is more expensive than alternative options on the market.
Pointing to its broader cross-industry framework, Report 798 Beware the gap: Governance arrangements in the face of AI innovation (REP 798), the corporate regulator emphasised that AI implementation must be matched by mature data governance, robust audit trails, and strict supervisory controls.
Statutory accountability remains with the broker
Crucially, ASIC clarified that no amount of technological sophistication alters or dilutes a credit assistance provider’s statutory obligations under section 158LA of the National Credit Act or Regulatory Guide 273 (RG 273).
Software engines and AI agents may streamline administration, but human professional judgement must remain at the core of every credit recommendation.
Licensees must be capable of auditing automated outputs, recording exact time-stamped datasets present at the time advice was provided, and explaining the logic behind any algorithmic recommendation.
To reinforce these expectations, ASIC concluded its evaluation of digital innovation by reminding the industry of the fundamental legal boundary governing AI adoption: “Outputs from any decision tools or AI agents should be explainable and documented (including the relevant data used to create them).
“While there may be significant benefits from further innovation, licensees must also ensure that their risk and governance arrangements keep pace with the adoption of AI.
“Brokers and licensees are still responsible for the quality of the credit assistance they provide, regardless of the technology they use.”
This warning reinforces earlier statements from ASIC commissioner Alan Kirkland, who cautioned against relying on automated documentation shortcuts when justifying loan choices: “When reviewing brokers’ recommendations, you should take extra care to ensure the reasons given make sense in the specific circumstances for the specific client. It’s not enough just to document them; they have to be personalised and meaningful. If the reasons for a recommendation are boilerplate factors that could apply to anyone, then it will be hard to demonstrate that the recommendation was in that customer’s best interests.”
[Related: APRA and ASIC roundtables urge action on frontier AI]
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Annie Kane
Author
Annie Kane is the managing editor of Momentum's mortgages titles: The Adviser and Mortgage Business.
As well as writing about the Australian broking industry, the mortgage market, financial regulation, fintechs and the wider lending landscape – Annie is also the host of the Elite Broker and In Focus podcasts and The Adviser Live webcasts.
Annie has a degree in English Literature and Music from the University of Sheffield and is a Mental Health First Aider (MHFAider®).
Email Annie at: