GLP-1 meds and AI are reshaping life insurance risk - what Munich Re's data means for your book
Global obesity has tripled since 1975. Munich Re's Life Science Report 2025 points to a counterweight: newer GLP-1 medications that could cut obesity-related morbidity and mortality at scale.
For life insurers, that's not theory. It's near-term shifts in underwriting inputs, mortality drift and product design.
Why GLP-1s matter now
GLP-1 agonists like semaglutide (Ozempic, Wegovy) and tirzepatide (Zepbound) are approved for weight management and used to treat conditions tied to excess risk: type 2 diabetes, high blood pressure, high cholesterol, cardiovascular disease and obstructive sleep apnea.
Munich Re also notes early benefits in metabolic conditions such as PCOS and obesity-related cancers. Across studies, these drugs show 15% to 21% weight loss - a level that moves mortality curves.
The dataset and the signal
Munich Re analyzed a de-identified U.S. dataset covering selective medical and prescription information on 41 million adults from 2015 through Jan. 1, 2025. In a Jan. 29, 2026 webinar ("Turn Evidence into Excellence"), leaders shared how these trends are feeding into underwriting.
Headline findings:
- GLP-1 adoption is linked to a 0.2% to 0.5% annual mortality improvement, realized over roughly 20 years.
- Projected over two decades: 21% mortality reduction for non-severely obese individuals and 40% for severely obese individuals in the general population.
As Timothy Meagher, vice president and medical director at Munich Re, Canada, put it: "Underwriting will be improved, and insurability should expand."
AI changes how we assess risk
AI is helping flag subtypes of type 2 diabetes (as shown by Stanford Medicine researchers) and model biomolecular interactions for quicker drug development. That shortens feedback loops between therapy, outcomes and pricing assumptions.
AI is also rewriting how we use health data. "By analyzing thousands upon thousands of electronic health records… you can actually begin to predict what that trajectory is going to look like way sooner," said Meagher. Expect richer EHRs, earlier risk signals and the rise of biological age to supplement chronological age in underwriting.
What insurers should do next
- Refresh mortality assumptions: scenario-test GLP-1 uptake, adherence and discontinuation. Incorporate 0.2%-0.5% annual improvement ranges and stress unequal access across income and geography.
- Tighten underwriting: capture GLP-1 use (drug, dose, duration), indication (obesity vs. diabetes), BMI/weight trend and key comorbidities. Consider pragmatic biological-age proxies using EHR-derived biomarkers.
- Upgrade data pipelines: stand up EHR ingestion, consent workflows and NLP on clinical notes to detect risk trajectories earlier.
- In-force actions: segment high-BMI blocks, monitor weight-loss durability and A1c trend, and watch for weight regain after discontinuation. Put guardrails in place to reduce anti-selection during rapid weight change.
- Product and pricing: offer preferred credits for verified sustained weight loss; consider re-underwriting windows or riders tied to maintaining improvements.
- Governance: run fairness and bias checks, document model explainability, and audit privacy compliance across EHR and prescription data.
Metrics to track
- GLP-1 initiation, adherence and discontinuation rates by segment
- BMI and A1c trajectories post-issue (EHR-verified vs. self-reported)
- All-cause and cardiovascular mortality vs. base tables; lapse behavior shifts
- Claim incidence, severity and time-to-claim in high-BMI cohorts
Key caveats for your models
- Access and cost can cap adoption; supply variability matters.
- Side effects and discontinuation may erode gains; monitor durability beyond 12-24 months.
- Selection risk: applicants could time coverage during rapid weight loss; design controls accordingly.
- Data drift: models trained on 2015-2025 patterns may understate post-2026 effects as access broadens.
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