How RBC Insurance Uses AI to Underwrite Faster and Catch Fraud
RBC Insurance reports that its AI platform, Clara (claims lifecycle automated recommendation assistant), prevented $2.4 million in fraudulent claims in fiscal 2025. It's also moving underwriting decisions faster and freeing specialists to work on complex files.
The intent is clear: improve profitability, speed up operations, and pass efficiencies into pricing. As Vik Luthra, chief digital, data and strategy officer, put it, "We scale that volume in a sustainable way that protects us from a business and earnings perspective."
What Clara Does Today
- Flags and filters suspicious claims at roughly 10x the speed of human review.
- Identifies forged documentation (including AI-generated doctor's notes) with higher accuracy.
- Underwrites term life policies up to $3 million for applicants under age 55, approving straightforward cases instantly and routing the rest to underwriters.
The result: fewer touchpoints on clean claims and applications, more attention on edge cases, and tighter loss control.
Why It Matters for Insurance Teams
Claims fraud is getting more sophisticated. Clara helps spot signal in messy medical files and multi-party disability cases. Faster detection means less leakage and less manual rework.
On the new business side, instant decisions on low-risk term life cases reduce cycle time and policy issuance costs. Underwriters stay focused on nuanced risk, not clerical triage.
Impact on Pricing and Profitability
RBC Insurance is explicit about the commercial goal. "We have target profitability that we want to achieve," Luthra said. Efficiency beyond that flows into pricing over time, creating room for more competitive premiums without compromising margins.
In the next five years, the company expects AI-driven benefits in the nine-figure range, tied to better advice, higher productivity, and sharper price points.
The Data and Systems Work Behind It
Getting here required a deep technology cleanup. Multiple legacy systems from past acquisitions-some decades old-were consolidated into a centralized client data store.
- Applications can now "talk" to each other reliably.
- Data is structured for AI consumption and auditability.
- Operational feedback loops (from claims and underwriting) improve models over time.
This foundation is what makes Clara's speed and accuracy possible-and scalable.
Roadmap: From Term Life to Enterprise-Wide
RBC Insurance plans to extend Clara's underwriting capabilities to all lines over time. The approach: automate high-confidence decisions, route the rest to specialists, and keep humans in the loop where judgment moves the needle.
Operational Takeaways for Carriers and Brokers
- Target specific bottlenecks first (e.g., disability medical reviews, simple life applications) to prove ROI.
- Tighten document forensics for AI-generated forgeries and require verifiable sources for medical evidence.
- Invest in clean, centralized client data-model performance depends on it.
- Build a clear triage policy: what gets auto-approved, queued, or escalated (with reason codes).
- Tie efficiency gains to pricing strategy and shareholder expectations early.
Fraud Context and Resources
Industry fraud trends are shifting fast, including synthetic identities and fabricated medical documentation. For a broader view on fraud indicators and consumer education, see the Insurance Bureau of Canada's guidance on insurance fraud here.
Upskilling Your Team on AI in Underwriting and Claims
If you're building internal capability around AI triage, document intelligence, or model oversight, explore role-based learning options here. A focused curriculum shortens the path from pilot to production.
The Bottom Line
Clara shows what happens when you pair disciplined data plumbing with targeted AI. Less leakage, fewer manual cycles, and pricing that reflects real efficiency-without diluting underwriting judgment.
That's the template: start where decisions are frequent and rules are clear, measure relentlessly, and expand coverage as the data-and trust-gets stronger.
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