Suncorp bets on AI and core renewal to tackle insurance affordability
Suncorp is doubling down on AI and a new policy platform to bring pricing within reach for more customers. The insurer says 2-4% of people in Australia and New Zealand can't obtain affordable cover today, and it's working with the federal government on an industry-wide fix.
The mandate: hold the line on pricing discipline while easing cost pressure for vulnerable segments. Technology is the lever. As CEO Steve Johnston put it, the goal is to get better at designing new policies, new premiums, and products for customers who are struggling to stay insured.
Inside "Digital Insurer" and the core overhaul
Digital Insurer is Suncorp's multi-year core transformation, centered on Duck Creek as the new policy administration system. It's already live for new home and motor business at AA Insurance in New Zealand, delivering simpler underwriting and more automation.
Next up is AAMI. Suncorp is well into release two, targeting mid-year for AAMI home and motor new business, with renewals migrating soon after. If execution holds, this gives Suncorp a cleaner policy stack, faster product changes, and lower handling costs.
The AI stack: multi-agent, embedded, and partner-led
Suncorp has history with AI and has ramped up work in multi-agent and agentic approaches, in part powered by Databricks. It plans to use native AI embedded in its core platforms: Duck Creek, Oracle, Earnix, Genesys, Adobe, and Salesforce.
Partners and BPO teams remain in the loop. The aim is end-to-end deployment-automation plus process redesign-so AI doesn't sit in a silo.
What this means for insurance teams
- Pricing and product: Expect micro-segmentation and faster rating iteration through Earnix and platform-native models. The focus is hyper-personalised cover that can lift affordability without blowing out risk. Guardrails for fairness and affordability will be key to avoid adverse selection.
- Underwriting: Simplified rules and straight-through processing on Duck Creek should cut touch time and leakage. Think clearer appetite, cleaner data capture, fewer referrals, and tighter expense control.
- Claims: AI-driven triage, assessment, and fraud cues can shorten cycle times and improve indemnity outcomes. Genesys and Salesforce integrations should lift customer communication quality while reducing manual hops.
- Distribution and service: AI can sharpen offer timing and next best action across the brand portfolio. The test is deeper engagement without driving churn through aggressive repricing.
- Risk and governance: Model risk management needs to keep pace-explainability, bias testing, and performance drift monitoring. Align with regulatory expectations on privacy and fairness across Australia and New Zealand.
- Data foundations: Reliable, consented, and well-modeled data underpins every use case. Event-driven integration between PAS, claims, and CRM will determine real-world speed.
- Change enablement: Frontline training, underwriting playbooks, and claims SOPs must be refreshed to reflect new automation paths. Metrics and incentives should reward quality and efficiency, not just volume.
Practical next steps for insurers
- Define an affordability measure for your portfolio and bake it into pricing and product reviews.
- Prioritise two to three AI use cases with clear P&L impact: claims triage, fraud, and retention pricing are proven starters.
- Stand up an embedded model operations routine-data pipelines, monitoring, and override rules tied to risk appetite.
- Plan a controlled migration path to modern PAS for one or two high-volume lines (home, motor) before scaling.
- Co-develop with vendors to use native AI inside your core platforms before you build net-new tools.
- Track weather-related volatility and reinsurance costs alongside loss and expense ratios to avoid masking benefits.
KPIs that matter
- Quote-to-bind and straight-through rate by segment
- Retention and churn versus premium adequacy
- Loss ratio, expense ratio, and leakage
- Claims cycle time, touch count, and settlement accuracy
- CSAT/NPS and complaint rate for vulnerable customers
- % of policies on the new platform and time-to-market for rating changes
- Share of customers meeting your affordability threshold
Suncorp posted $263 million NPAT for the half, hit hard by extreme weather events. That context makes the affordability push more urgent: lower handling costs, smarter pricing signals, and faster claims can protect both customers and margins.
For deeper context on practical AI plays across underwriting, pricing, claims, and service, see AI for Insurance. If you're building out new product constructs and rating models, this primer on AI for Product Development can help pressure-test your roadmap.
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