RBC Insurance's AI Playbook: Governance, Agility, and Client Focus That Delivers
Fraud is getting smarter. Margins are tighter. Clients expect faster, fair decisions. RBC Insurance is answering with a simple formula: build AI in small, well-governed steps, prove value quickly, and keep people at the center.
Start small, move fast, prove value
Vik Luthra, chief digital, data & strategy officer at RBC Insurance, credits an agile mindset for the early wins. The goal wasn't "nice demos." It was measurable outcomes with guardrails from day one.
Case in point: CLARA - the Claims Lifecycle Automated Recommendation Assistant. In its first year, while still a pilot, it flagged and helped prevent more than $2 million in fraudulent claims. That's real money, reinvested into better pricing and protection for clients.
AI augments specialists, it doesn't replace them
RBC's approach is clear: AI gives claims experts better indicators and faster signal, but humans make the call. Their claims team includes professionals with healthcare backgrounds who manage complex cases with empathy and precision. AI speeds the work; it doesn't sideline judgment.
Governance first, then scale
Speed doesn't come from skipping steps. It comes from a strong base: centralized data, clear ownership, and risk controls baked into every stage. RBC formalized Responsible AI principles across privacy and security, accountability, fairness and transparency, and responsible disclosure.
- Compliance is built into the lifecycle: testing, validation, monitoring, and continuous improvement.
- Models and tools are reusable across products to scale safely and reduce duplicate effort.
If you're building your own framework, the NIST AI Risk Management Framework is a solid reference for industry-standard controls.
Fraud trends and the downstream impact
Fraud is rising across insurance. That pushes up capital needs and pressures pricing. Removing fraudulent claims doesn't just protect the book; it improves pricing models and lets you offer stronger coverage without hidden costs from leakage.
Startup discipline inside a big institution
Luthra's philosophy: experiment with guardrails, ship iteratively, and hold a laser focus on client value. Be ambitious, but responsible. That means clear risk boundaries, fast feedback loops, and readiness to scale what works.
Enterprise-level commitment
RBC treats AI as a multi-year capability, not a tool of the week. Since launching Borealis AI in 2016, they've invested in data scale, top talent, a culture of innovation, and high-grade security. The result: faster movement with more confidence - without trading away stability.
A practical playbook insurers can apply now
- Prioritize use cases with measurable ROI: claim fraud scoring, SIU triage, FNOL document extraction, contact center assist.
- Set guardrails upfront: data minimization, privacy-by-design, human-in-the-loop, clear escalation paths.
- Define success metrics before launch: fraud savings, cycle time, NPS, false positive rate, investigator productivity.
- Stand up a centralized data layer and model registry so teams reuse components instead of rebuilding.
- Embed model risk management: pre-prod testing, bias checks, drift monitoring, and periodic revalidation.
- Train claims, underwriting, SIU, and compliance teams together so the tools fit real workflows.
What will separate leaders from laggards
- Strong governance and risk management that enables speed, not bureaucracy.
- An AI-ready workforce that understands where to apply AI and how to supervise it.
- Clear priorities tied to client value - fewer experiments, more wins.
If you're building team capability, explore practical programs to upskill by role: AI courses by job. For tooling awareness, see curated AI tools for finance.
The takeaway: start with governance, move with intent, and let your experts lead with AI as their accelerator. That's how RBC Insurance turned pilots into dollars - and trust into a moat.
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