Manulife Canada Uses AI to Cut Life Insurance Approval Times to Minutes
Manulife has launched a redesigned digital life insurance application and an upgraded AI underwriting engine that can approve eligible Canadian applicants in as little as two minutes. At the core is MAUDE - the Manulife Automated Underwriting Decision Engine - built to simplify submissions for advisors and accelerate decisions without sacrificing quality.
Early adoption has been strong. "By December, more than half of eligible cases - 58 per cent - had approvals processed automatically through MAUDE, a 56 per cent increase from pre-launch," said Karen Cutler, Chief Underwriter, Manulife Canada.
What changed
- Reflexive, physician-style questions that zero in on relevant details faster.
- Up to 40% fewer medical questions, reducing friction and completion time.
- Adaptive forms that adjust by age and coverage amount.
- Standardized pick lists for medications and conditions to improve data quality.
- Flexible workflows for cases that need paramedical exams.
If a case can't be auto-approved, it routes to a human underwriter for a personalized review. The goal: let the engine handle clean cases so underwriters can focus on nuanced risk.
Why this matters to insurers and distributors
- Throughput and cycle time: Faster triage moves clean business through instantly, freeing capacity for complex assessments.
- Data consistency: Structured pick lists and reflexive logic reduce free-text variance, improving auditability and downstream analytics.
- Advisor experience: Shorter, smarter applications cut drop-off and rework, supporting higher placement ratios.
- Operational control: Clear handoffs for parameds and exceptions keep cases moving without back-and-forth.
For additional examples and best practices specific to the sector, see AI for Insurance.
"Every enhancement is grounded in our Responsible AI Principles, so advisors and clients can feel confident that decisions are accurate and responsible," said Jodie Wallis, Global Chief AI Officer at Manulife.
Governance and risk points to track
- Model governance: Document decision logic, controls, and monitoring for drift and bias. OSFI's model risk expectations remain the bar for federally regulated insurers.
- Adverse action workflows: Maintain clear audit trails for escalations and declines with accessible rationale for advisors and clients.
- Data lineage: Standardized inputs help, but you still need lineage from application to decision for reviews and complaints.
- Reinsurance alignment: Keep treaty rules and evidence requirements synchronized with automated pathways.
For context on Canadian expectations, see OSFI's Model Risk Management guidance here.
What advisors and underwriters can do now
- Pre-screen efficiently: Use the reflexive flow as intended; avoid free-text that triggers unnecessary referrals.
- Tighten disclosures: Coach clients on medication names, dosages, and timelines to minimize clarifications.
- Escalation readiness: For non-auto approvals, anticipate what evidence will speed a decision (APS, labs, attending notes).
- Feedback loop: Flag recurring friction points to product and tech teams so the question set keeps improving.
The business impact
Manulife has been scaling AI since 2016 and expects more than $1 billion in enterprise value from AI by 2027, with a large share from operational efficiency. Faster, cleaner underwriting improves placement, lowers acquisition costs, and tightens SLAs across distribution.
Bottom line
MAUDE's two-minute approvals show how automation can lift both speed and consistency while keeping complex risk with human experts. If you run underwriting, distribution, or operations, the playbook is clear: simplify inputs, automate the predictable, and reserve specialist time for judgment calls.
If your team is building skills for AI-enabled insurance operations, explore the AI Learning Path for Business Unit Managers or practical learning paths by role here.
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