Generic AI creates advice risk. Controlled workflows reduce it.
Advisers are already using general-purpose AI in client work. The licensee exposure is whether the file can be reproduced, reasoned and defended on review — a reasoning-chain question now squarely inside the best-interests obligation, and the point at which weak practice becomes financial liability when it repeats at scale.
Adviser-supervised by design. Finlogica does not provide personal financial advice. Authorised advisers and licensees retain advice scope, client-facing judgement, approval and record-keeping responsibility.
"ASIC is urging financial services and credit licensees to ensure their governance practices keep pace with their accelerating adoption of artificial intelligence."ASIC Media Release 24-238MR · 29 October 2024
Firm risk rises as control breaks down.
Each step increases the probability that the file cannot be reconstructed, justified or defended — and increases the chance of financial liability and reputational harm if the weakness repeats across the firm.
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Click any bar to read each risk in detail.
No audit trail or reproducibility
The file often cannot demonstrate how advice was formed, what inputs were used, what logic was applied, or why the recommendation was appropriate.
This directly impacts personal advice record-keeping obligations and reconstruction under regulatory review.
Without reproducibility, identical inputs may produce different outputs. That is a best-interests risk, not just a documentation issue.
Client data exposure
Client PII data may be entered into general-purpose models. There may be no enforceable limits on retention or secondary use, with potential offshore processing or onward disclosure, and use of client data in model training.
This creates a live Privacy Act exposure under APP 11 (security of personal information) and APP 8 (cross-border disclosure).
Hallucination and inconsistent outputs
Language models can generate plausible but incorrect content, misinterpret financial context, and may produce different outputs from the same facts, and will usually fail to balance portfolios to 100.0% or exact dollar amounts.
The output may appear correct — without being correct.
Systemic failure modes
When a prompt or tool fails, the failure can repeat the same way across multiple advisers and client files before the issue becomes visible.
That is not a one-off complaint. It can become a systemic issue, a reportable situation, and concentrated CSLR exposure. The risk is not visible at the point of advice creation.
From file weakness to firm-level harm.
Unmanaged AI use does not stop at one file. At scale it can turn into financial liability, remediation cost and reputational harm across the licensee. The CSLR levy and AFCA determinations follow the licensee, not the tool.
no pre-release compliance checks
no alignment to your control framework
no systematic validation against best-interest and suitability requirements
What they close
Enterprise AI tools deployed inside a firm's tenant — Microsoft Copilot, enterprise ChatGPT and similar — can reduce some privacy and data-residency exposures relative to consumer AI use. Privacy Act APP 8 and APP 11 alignment becomes easier to argue.
What they leave open
The file still cannot be reconstructed. The reasoning chain is not retained. Calculations remain inside narrative. Weak prompts still repeat across files. The best-interests obligation question — under Corps Act ss 961B and 961G — is unchanged.