Industries · Financial Services
AI in Australian financial services, governed for APRA.
Lastmile for APRA-regulated entities — runtime governance, audit chain, and risk-tiered delivery. Built for CPS 230, AUSTRAC explainability, and ASIC conduct evidence.
THE PROBLEM
Three things FS leaders keep telling us.
AI ambition is real. The blockers are familiar — and structural.
Paperware governance
AI policy sits in a PDF approved by the AI Council but never enforced at runtime. Internal audit asks for evidence that policy was followed on a live transaction — and the evidence doesn't exist.
No audit chain across AI interactions
Model usage, data accesses, and decisions are scattered across vendor consoles, application logs, and email approvals. Assembling an audit response takes weeks and is incomplete every time.
Use cases stalled in risk review
Promising AI use cases get parked because the second-line risk function can't get comfortable with the controls. Without a defensible control pattern, every new use case re-litigates the same fights.
REGULATORY ALIGNMENT
How we align to APRA, AUSTRAC, and ASIC.
Each regulator expects something specific. Lastmile produces the runtime evidence that satisfies it.
Regulator
APRA
Banks, insurers, superannuation. Operational risk via CPS 230 and CPS 234.
What they expect
AI systems supporting critical operations are mapped, risk-tiered, and tested for resilience. Third-party AI services are governed under CPS 230 service-provider arrangements with documented impact tolerances.
Where Outpace plays
Outpace AI's Control phase produces the CPS 230-aligned operational evidence: critical-operations mapping, third-party service register entries for AI vendors, and tested impact tolerances.
Regulator
AUSTRAC
AML/CTF program oversight. AI in transaction monitoring and customer due diligence.
What they expect
AI-assisted alerts and decisions are explainable, with clear boundaries between AI-recommended actions and human-approved actions. Model risk and validation are documented.
Where Outpace plays
AI Security & Compliance covers the explainability evidence chain and the human-in-the-loop architecture AUSTRAC expects in regulated AML/CTF workflows.
Regulator
ASIC
Financial services conduct. AI in advice, retail product distribution, and disclosure.
What they expect
AI used in any conduct-touching process is governed for fairness, accuracy, and consumer protection — with evidence of testing, monitoring, and customer-impact assessment.
Where Outpace plays
Outpace AI's evals discipline (built in AI Delivery & Assurance) produces the testing and monitoring evidence ASIC asks for, with portfolio-level reporting across customer-facing AI.
HOW WE SERVE FS
Three Lastmile entry points for financial services.
WHAT GOOD LOOKS LIKE
Sample engagement · Anonymised
Customer service copilot for a top-5 AU bank.
A retail-banking copilot that handles tier-1 customer enquiries. Risk-tiered as "high-impact, customer-facing" in Qualify. Built with runtime explainability, full AUSTRAC alignment for any AML touchpoint, and a CPS 230-mapped operational profile. Control phase publishes a monthly scorecard to the AI Council with policy-violation rate, cost per resolved enquiry, and quality regression flags.
FINANCIAL SERVICES FAQ
Questions FS leaders ask us most
Outpace AI works as a service provider to APRA-regulated entities and supports clients to onboard us into their CPS 230 service-provider register. We're familiar with the documentation requirements and impact-tolerance testing that come with that.
We work with banks and insurers of varying sizes in Australia. Specifics are covered under NDA. The patterns we use — risk tiering, CPS 230-aligned controls, evals discipline — are the same regardless of institution size.
We architect AI systems to keep data in Australian regions by default. Where a use case requires global model providers (e.g. frontier model APIs), we wire in policy controls so that regulated data never leaves the agreed boundary, with runtime evidence to prove it.
Yes. As part of Lastmile, we produce the documentation and operational evidence that lets internal risk and operational-resilience teams onboard AI capabilities under CPS 230 — including impact-tolerance specifications, dependency mapping, and tested failover patterns.
Architecturally. We don't bolt explainability onto a black-box model — we design the AI system so that every decision has a traceable rationale, with clear separation between AI-recommended actions and human-approved actions. The explanation surface is part of the system, not a generated artefact.
Both. If you already have an AI Council, we work to its decision rights and reporting cadence. If you're standing one up, the Qualify phase produces a charter, RACI, and reporting cadence as artefacts.
We're vendor-neutral and have shipped Lastmile on Azure OpenAI, AWS Bedrock, Vertex AI, Anthropic direct, and self-hosted open-weights models. The Control layer abstracts model choice — so you can switch providers without re-doing governance.
Qualify is two weeks. Activate is typically 4–6 weeks for a financial-services use case (longer than the standard 3–5 because risk review cycles are deeper). Control is ongoing.
Yes. We routinely complete the full enterprise security review (third-party risk assessment, penetration test review, ISMS attestation) before any production data is touched. ISO 27001-aligned posture is the baseline.
FS engagements are led by a senior Lastmile lead with prior regulated-FS experience, supported by a governance architect and embedded engineers. We don't rotate juniors onto regulated engagements.
Ready when you are
Ready to Outpace?
Book a 30-minute discovery call with the Lastmile team. No pitch decks, no pressure — a focused conversation on where AI can move the needle for your organisation, and whether the structured operating model is the right fit.