Insurers fast-track AI to production as OpenAI demand soars

Insurers accelerate AI rollout as OpenAI demand surges

December 5, 2025 - Insurers and banks are pushing AI from pilots to production. OpenAI reports rising demand from insurance and financial services as generative systems become the core operating layer for fraud checks, claims handling, customer support, and risk analytics.

European organisations are already seeing measurable gains as deployments scale. Names on OpenAI's customer list include Revolut, Allica Bank, Hg, EQT and Permira, with new performance data from Zopa and OakNorth. Deployments also span brands like BBVA and NatWest.

"AI has moved from pilots to production, and we're seeing that regulated industries aren't lagging behind - they're leading the way," said OpenAI EMEA solutions engineering lead Matt Weaver. "Financial firms are showing how highly regulated companies can succeed with the right governance, strategy, platforms and support in place."

What this means for insurers

• Faster decisions: Routing, triage, and policy/claim summaries done in seconds instead of hours.
• Better fraud controls: Pattern spotting across documents, voice/chat logs and historical claims.
• Lean operations: Automated first-pass reviews and AI copilots reduce manual workload and queue backlogs.
• Personalised experiences: Context-aware support and proactive guidance across channels.

European leaders show the playbook

• Revolut: Embedding GPT-5 to strengthen financial crime controls and upgrading its customer assistant (Rita) on a fine-tuned GPT-4.1.
• Allica Bank: AI-driven insights and automated first-pass application assessments, saving up to 20 minutes per case.
• OakNorth: ~90% employee adoption of OpenAI tools; 360+ custom GPTs for HR, compliance, legal and asset management; tens of thousands of hours saved annually.
• Zopa: AI reviews ~40,000 customer interactions monthly to flag vulnerability; piloting an AI Banking Assistant with 200,000 customers.
• Permira: ChatGPT Enterprise for investment analysis, risk and operations; expects $450m in AI-related portfolio revenue, with 90% of the team as regular users (4x growth in a year).
• EQT: Responsible AI at scale; ~90% of 2,000 employees use ChatGPT weekly; average of 45 minutes saved per person per day via 350+ internal GPTs.

A practical rollout plan for insurance teams

• Pick three high-ROI use cases: Claims FNOL summarisation, fraud triage, and agent assist for customer queries.
• Contain risk early: Use enterprise controls, data isolation, role-based access, and clear human-in-the-loop steps.
• Standardise prompts and workflows: Create internal GPTs for repeat tasks (claims notes, subrogation checks, coverage validation).
• Measure everything: Time saved per task, claim cycle time, fraud hit rate, false positive/negative rates, CSAT, AHT, and regulatory findings.
• Train the frontline: Short, role-specific playbooks for adjusters, SIU, underwriting assistants, and service teams.
• Close the loop: Weekly error reviews, prompt updates, and exception routing to experts.

Governance that satisfies audit and regulators

• Policy and access: Clear usage policies, audit trails, data retention rules, and red-teaming for sensitive workflows.
• Model risk management: Document intended use, known limits, fallback paths, and monitoring thresholds.
• Privacy and security: Minimise PII exposure; segregate training vs. inference data; keep humans accountable on final decisions.

For context on Europe's regulatory direction, see the EU AI Act overview. For enterprise deployment patterns, review ChatGPT Enterprise.

90-day execution checklist

• Week 1-2: Confirm use cases, set success metrics, and secure an enterprise AI environment.
• Week 3-4: Build internal GPTs and prompt libraries; integrate with claims/CRM knowledge bases.
• Week 5-8: Pilot with small teams (adjusters, SIU, contact center); collect errors and edge cases.
• Week 9-12: Harden controls, expand access, automate reporting, and lock in change management.

Bottom line

Insurers that move now are getting a measurable edge: faster claims, fewer fraud losses, and better customer outcomes-delivered with strong governance. The path is clear: pick focused workflows, stand up secure tooling, and scale what proves its value.

Helpful next steps

• Build internal GPTs and playbooks with curated training: AI courses by job
• Explore practical patterns for custom GPTs in operations: Custom GPTs resources