Industry · Financial Services
AI work for banks, insurers, super funds and wealth managers — with the APRA CPS 234, CPS 230 and Privacy Act compliance reality baked in from the start.
AI work in Australian financial services hits a wall every Big 4 consultancy knows but few say plainly: APRA expectations apply from week one, the data lives in older systems than the demo videos assume, and the operator who'd run the system is usually a frontline rep whose feedback wasn't requested. We design AI engagements for the AU FS reality. CPS 234 and CPS 230 are first-class concerns, not afterthoughts. Risk registers map to the controls your internal audit team already tests. The board pack is written so the chair doesn't need translation.
Regulatory context
Every AI engagement we run in Financial Services produces documentation that explicitly maps the work to the obligations below. The risk register, the control framework, the board pack — they reference these by name, so internal audit and compliance teams can adopt the artefacts directly without translation.
Key challenges
AI workflows touch information security (CPS 234) and operational risk (CPS 230) the moment they go live. We map AI-specific risks to existing control frameworks so internal audit can adopt them directly — rather than building a parallel AI risk register that doesn't talk to the rest of the firm.
AU FS firms can't default to whatever a US-based vendor pitches. Bedrock and Azure both offer AU-region deployment; some open-weights models work in VPC; some use cases tolerate global APIs with the right contractual protections. The decision is rarely "pick the best model" — it's "pick the model that works with your governance posture."
The classic AI-in-FS failure: a brilliant tool nobody uses. The fix is engineering-level: the operator sits in the workshop on day one, the metric we optimise is the one they care about, and the system surfaces inside the tools they already live in rather than as a separate UI.
AI run costs grow with usage in non-linear ways. The CFO conversation that prevents a quiet abandonment in month 9 is the one that happens in month 1 — with a 12-month run-cost model, scenario analysis, and an agreed unit economic the system has to hold.
Use cases
Triage, summarisation, draft-response generation for service teams. Measurable: AHT reduction, first-contact resolution rates, agent satisfaction scores.
LLM-assisted investigation tools for fraud analysts. Not "AI detects fraud" — instead, "analyst is 3x faster on case workup because evidence assembly is automated."
Document analysis, edge-case detection, policy-compliance checking. Designed for human-in-the-loop, not autonomous decisioning.
RAG over policy + regulation corpora. The pattern that's genuinely transformative for in-house legal and compliance teams.
Services most relevant here
Practical AI governance for Australian businesses — policy, risk registers, board reporting, and the audit trail that satisfies both your CISO and your CEO.
Explore practice
From "we should do something with AI" to a prioritised, costed roadmap your team can actually deliver — with the people who would run it sitting in the workshop.
Explore practice
RAG, agents, evaluations and observability designed for the realities of running LLMs in production — cost, latency, accuracy and drift, all measured.
Explore practice
FAQ
Yes. Insurance work tends to skew toward underwriting + claims AI; super work tends to skew toward member-service AI and compliance automation. The methodology is the same; the use cases and regulators differ.
Every AI governance engagement we run for an APRA-regulated entity produces documentation mapped explicitly to CPS 234 and CPS 230 (and CPG 235 where applicable). Internal audit can adopt the artefacts directly rather than translating from a generic AI risk register.
Next step
We’ll come ready with questions specific to your industry and your regulator environment. 30 minutes, conversational, no commitment.