AXA

Traditional life insurance underwriting was a friction-heavy process that required applicants to schedule medical exams, submit to blood draws, and wait days or weeks for a coverage decision. For a 100-person insurtech operating inside MassMutual's infrastructure, this status quo represented both a competitive disadvantage and a structural modeling challenge: mortality is a rare, slow-moving event, meaning any ML model built to predict it requires decades of outcome data to validate. Without access to that historical depth, Haven Life could not build actuarially sound risk models at scale — and without fast underwriting, it could not differentiate in a market increasingly demanding digital-first experiences.

Haven Life addressed both constraints by building predictive ML models on top of MassMutual's decades of accumulated mortality, medical, and actuarial data — a dataset that took generations to assemble and cannot be replicated quickly by competitors. Rather than applying traditional threshold-based actuarial rules (e.g., flag anyone with blood pressure above X), the models capture non-linear interactions between lab biomarkers — blood pressure, albumin, globulin, and others — to produce a composite mortality risk signal. A team of approximately 40 MassMutual data scientists supported the modeling effort. These models power Haven Life's 'InstantTerm' product, which runs applicant data through the underwriting engine at the point of application, enabling fully algorithmic decisions without human review or medical examination.

Haven Life became the first life insurer to deliver coverage decisions in approximately two minutes with no medical exam required — a step-change from the industry standard of days to weeks. Key outcomes include:

• Underwriting decision time: Reduced to under 2 minutes from days or weeks
• Exam elimination: No medical exam required for qualifying applicants
• New medical insights: Models identified that multiple simultaneously low lab values carry mortality risk comparable to individual high values — a signal invisible to traditional rule-based methods
• Expanded variables: Haven Life has since extended its models to incorporate credit data and prescription histories, broadening predictive signal over time

• Proprietary historical data compounds in value: MassMutual's decades of mortality records gave Haven Life a modeling foundation competitors cannot quickly replicate — the data moat is as important as the model itself.
• ML surfaces non-linear medical relationships that rules miss: Threshold-based actuarial logic cannot capture interaction effects between biomarkers; gradient-based models can.
• Plan validation timelines around the event being predicted: Mortality is rare and slow; teams building similar models should budget years, not sprints, for outcome validation.
• Institutional backing enables insurtech speed: Haven Life's access to MassMutual's data science team and historical records compressed a capability that would otherwise take decades to build independently.