AI Governance Advisory · Australia
Your board is accountable for AI decisions it cannot yet see.
V Prosper translates AI behaviour into APRA-, FAR- and AFCA-recognised language — and builds the evidence your board needs before a regulator, an accountable person, or a customer asks who signed off.
| Model | Function | Reg. exposure | Board sign-off |
|---|---|---|---|
| PRX-001 | Premium pricing | CPS 230 §7.3 FAR s.32 | Signed 24 Mar |
| UW-047 | Underwriting score | CPS 230 §5.1 FAR s.28 | Awaiting sign-off |
| CLM-012 | Claims triage | AFCA Rule A.4 | Awaiting sign-off |
| UW-051 | Fraud detection | CPS 230 AFCA | Not registered |
The exposure
Three gaps boards can no longer leave open.
AI is already pricing, underwriting and deciding claims. The regulatory expectation of oversight has arrived ahead of most boards' ability to demonstrate it.
Model oversight APRA can ask for.
APRA's operational risk framework expects firms to identify, assess and control the risks of the systems driving their decisions. Most mid-tier insurers have no model inventory, no board-visible sign-off record, and no reporting line connecting AI outputs to the oversight body accountable for them.
Accountability a named person can defend.
Under FAR, accountable persons are personally liable for failures within their remit — including failures of AI systems that influence material decisions. Most named individuals cannot yet point to the statements, evidence packs, or escalation records that demonstrate genuine control rather than assumed oversight.
Decisions AFCA can investigate.
AFCA expects insurers to explain outcomes — including outcomes shaped by AI. When a complainant asks how a pricing or claims decision was reached, a firm needs to reconstruct what the model did, why, and who was accountable for it. Most firms currently cannot.
Engagements
Three ways to close the gap.
01 · Diagnostic
Board AI Oversight Diagnostic
A short, fixed-scope review that maps where AI is influencing decisions across pricing, underwriting and claims — and translates each use into concrete APRA, FAR and AFCA exposure your board can act on.
Learn more →02 · Build
Model Inventory & Sign-off Framework
We design and stand up the governance infrastructure — model inventory, ownership mapping, sign-off templates, and oversight cadence — that turns ad-hoc model use into something a board can supervise.
Learn more →03 · Assurance
AFCA-Defensible Decision Logging
For each AI-influenced customer decision, we define the logging and explanation artifacts that let your team reconstruct and defend what the model did — before AFCA or a complainant asks.
Learn more →Process
How a typical engagement works.
Scope
A single working session to define the engagement boundary — which models, which regulatory obligations, which stakeholders — so nothing drifts.
1–2 days
Diagnostic
We map where AI is making or shaping decisions, identify the regulatory exposure at each point, and surface the evidence gaps that matter most.
1–2 weeks
Evidence Build
We draft the artifacts — model inventory, sign-off records, accountability statements, decision logs — in the form regulators and accountable persons recognise.
2–3 weeks
Board Readout
We present findings and hand over a complete, board-ready pack — written for the people accountable, not for the team that built the models.
1 week
Why V Prosper
We translate AI behaviour into APRA-, FAR- and AFCA-recognised language.
- We don't sell AI software or take commissions on tools we recommend.
- We don't audit code or validate model performance.
- We don't write 200-page reports nobody reads.
- We produce the specific evidence artifacts that let a board demonstrate it was in control.
The difference
Built for the people accountable, not for the people who built the models.
Most AI governance work is done by the same technical teams that built the models — which means the oversight artifacts are written in a language boards can't act on and regulators won't recognise as governance. V Prosper works directly with boards, audit committees, and accountable persons.
Every deliverable is scoped tight and designed to be usable on the day it's handed over — not a starting point for another internal project. You leave each engagement with something your board can put in front of APRA, AFCA, or your own risk committee tomorrow.
We also move fast. Mid-tier insurers don't have the internal resource to run multi-month governance programs. A typical engagement completes in four to six weeks, producing board-ready evidence that a larger firm would take a year to produce.
Insights
The regulatory landscape, plain.
CPS 230 · Operational Risk
CPS 230 and the AI accountability gap: what APRA expects, and what most insurers can't yet show.
APRA's updated operational risk framework applies directly to the models pricing your policies and scoring your claims. Most boards have no idea what evidence they'd need to produce if asked.
Read →FAR · Accountable Persons
Who signs off on the model? FAR accountability for automated underwriting and claims decisions.
The Financial Accountability Regime names individuals. When an AI system makes a material decision, someone has to own it — and be able to prove they were in control of it.
Read →AFCA · Dispute Resolution
When AFCA investigates an AI-influenced claim: what the rules require and what firms currently can't show.
AFCA's rules require insurers to explain decisions. For AI-influenced outcomes, that explanation has to go beyond "the model said so." Most firms cannot reconstruct the decision trail.
Read →Start the conversation
If a regulator asked your board to evidence AI oversight tomorrow, could it?
In a 20-minute call, you'll leave with a one-page exposure map across CPS 230 FAR AFCA — at no cost, no preparation required.