AI Consulting for Financial Services

If you are an APRA-regulated entity, two standards do most of the work in an AI conversation. CPS 234 covers information security and requires you to maintain capability commensurate with the threats, clearly define roles and responsibilities, and — importantly for AI — manage the security of information assets handled by third parties. An AI vendor processing your customer data is a third party holding an information asset, and that pulls the deployment squarely into scope. CPS 230 covers operational risk management, service provider management and business continuity, and it matters because an AI system embedded in a business process becomes an operational dependency: if the model provider has an outage or changes behaviour under you, that is your operational risk, not theirs. The practical consequence is that AI vendor selection in a regulated entity is a material service provider decision with all the assessment that implies, not a software purchase. We help establish the facts for that assessment. We do not perform it, and we do not certify your compliance with any standard — your risk and compliance functions own that, and no consultant can take it from them.

ASIC has publicly reviewed how licensees are governing AI and has flagged that governance arrangements have in cases lagged behind adoption — the pattern being that businesses deploy first and construct the governance afterwards, if at all. The underlying obligations are not new and they are not AI-specific: an AFS licensee must provide financial services efficiently, honestly and fairly, must have adequate resources and risk management systems, and must comply with its design and distribution obligations. None of those acquire an exception because a model was involved in the decision. What AI changes is the difficulty of evidencing them. If a model contributes to a decision affecting a customer, you should be able to explain the basis of that decision, demonstrate it was not discriminatory, reproduce it if challenged, and show a human was accountable. That is an engineering requirement as much as a compliance one — it has to be designed in at the start, because retrofitting explainability to a deployed system is expensive and usually incomplete. Confirm the current regulatory position with your compliance function rather than a consultant’s website, including this one.

The reliable answer is: everywhere the customer outcome is not being determined. Internal document review, policy and procedure retrieval, correspondence triage, meeting and file note preparation, data extraction from statements and application documents, first-pass reconciliation, and the enormous volume of internal reporting that consumes analyst time. All of it is high-volume, evidence-friendly, and none of it decides whether someone gets a loan, a payout or advice. The moment a model influences a customer outcome — credit decisions, pricing, claims, advice, suitability — you have moved into a category with explainability, fairness, DDO and record-keeping obligations attached, and the engineering cost of doing it properly is several times the cost of doing it badly. Most firms have not exhausted the first category, which is why we recommend staying in it. It is also where the return arrives soonest, so the conservative path and the profitable path happen to coincide.

Generally yes, but the answer has to come from the contract and the architecture, not the marketing page. The questions that decide it: where does inference physically occur and in what jurisdiction; are prompts and outputs retained, and for how long; are inputs used to train or improve the vendor’s models; who at the vendor can access content and under what circumstances; what subprocessors are involved and where do they sit; what compelled-disclosure exposure does the vendor’s jurisdiction create; and what happens to your data on exit. Under APP 8 you remain accountable for personal information disclosed overseas, and under CPS 234 you are responsible for information assets managed by third parties — so both regimes point at the same set of questions. The pattern we see repeatedly is a firm making this decision from a vendor’s security page while the data processing agreement says something materially different. Reading the actual agreement is unglamorous and it is most of the value.

Larger regulated entities usually end up with both, and the split that works is fairly consistent. Build internally where the capability is durable, strategically central, and needs deep knowledge of your systems, your risk appetite and your regulatory posture — that knowledge does not transfer well and you will need it permanently. Engage externally for the diagnosis, for the first implementations that establish patterns, and for the specific expertise you need occasionally rather than continuously. The failure mode we see most is hiring a small internal team before anyone has decided what they are for, then watching them spend eighteen months on a platform nobody asked for. The sequence that works better is: diagnose, ship one thing, learn what the work actually requires, then hire against that knowledge. Our comparison of the two paths works through the costs honestly, including the cases where in-house genuinely wins.

A free initial consultation to start — a conversation about where the operational load sits and what your risk appetite actually permits, not a demonstration. If there is a real opportunity, the next step is generally the AI Opportunity Audit at around $3,000: a written map of your workflows, the systems and data behind them, the regulatory constraint attached to each, and a ranked shortlist with realistic effort and payback, explicitly separated into work that does and does not touch customer outcomes. That separation is usually the most useful thing in it for a regulated entity. You keep the audit either way. Where it stacks up, we build and ship the first workflow into production rather than handing over a report. To be explicit about our limits: we are AI consultants, not compliance advisers, and nothing we produce certifies or guarantees your position under any prudential standard or licence condition.