Consumer wearables and connected health apps have matured from simple activity counters into continuous, clinical‑grade signal sources capable of informing risk assessment, early detection, and personalized interventions. For life insurers, this creates a strategic inflection point: data from wearables and health apps enables:

• finer-grained underwriting and pricing,
• behavior‑driven incentive programs that measurably change risk exposure, and
• new revenue streams positioned around wellness services and longevity management.

However, the promise carries material operational, actuarial, and regulatory complexity - including fairness and exclusion risk, privacy and consent management, model governance, and the paradox that successful wellness programs can increase life expectancy (creating durability/liability for annuity and reserving functions).

Purpose: To provide life‑insurer executives, product teams, actuaries, compliance, and innovation leads with a market‑researched guide to designing, testing, and scaling wearable‑enabled life insurance propositions that deliver health value to customers while managing insurer financial risk.

Scope: Focuses on consumer wearables and companion smartphone apps that collect continuous physiological and behavioral signals (steps, heart rate, HRV, sleep, ECG, SpO₂, continuous glucose, location/mobility proxies). It examines use cases for underwriting, pricing, wellness incentives, claims, and longevity management rather than clinical device certification or medical device regulatory pathways. The white paper assumes pilots and clinical validation, where required, prior to risk decisions that have a material pricing impact.

Wearables now provide a spectrum of signals:

• Activity & Movement: steps, cadence, sitting vs standing, gait analytics
• Cardiovascular: continuous heart rate, heart‑rate variability (HRV), single‑lead ECG traces (AF detection), heart‑rate recovery
• Sleep: sleep stages, fragmentation, sleep duration, and regularity
• Respiratory / SpO₂: oxygen saturation and sleep apnea screening proxies
• Metabolic: continuous glucose monitoring (where available)
• Other: skin temperature, galvanic skin response, fall detection

Data quality varies by sensor, device class, and context. Consumer devices provide high‑frequency proxies suitable for behavioral signals and trend detection; some newer devices have clinical‑grade modules (ECG, pulse‑ox) but remain regulated differently by jurisdiction. Insurers must quantify signal reliability (coverage, missingness patterns, device bias) and implement device‑agnostic analytics layers that normalize and quality‑score incoming streams.

Insurers have piloted three broad program archetypes:

1. Rewards & Engagement Programs (Vitality‑style): Programmatic incentives (discounts, device rebates, vouchers) for measured healthy behaviors - represented by Discovery/Vitality and John Hancock Vitality. These focus on enrolment, engagement, and retention while aligning incentives to activity targets.
2. Accelerated Underwriting / Simplified Issue: Collecting short bursts of wearable data to accelerate or replace medical paramedical requirements for new business.
3. Continuous Underwriting / Dynamic Pricing Pilots: Experimental programs that consider periodic premium adjustments or tier movement based on longitudinal data.

The platforms are maturing - reinsurers and research houses (Munich Re, Swiss Re, RGA) publish frameworks and pilot evaluations recommending careful validation and robust governance before using wearables for high‑stakes pricing decisions.

Use case 1 - Accelerated underwriting (onboarding acceleration).

What: Applicants share a short window (7–14 days) of wearable data to enable faster risk assessment.
Why it works: Objective behavioral data can replace or complement medical disclosures and reduce friction for digitally‑native cohorts.
Design considerations: Bias mitigation (device ownership skew), consent transparency, fallback for applicants without compatible devices.
Worked example: 14‑day step + resting heart rate collection used to validate lifestyle risk claims and reduce need for paramedical exam in 45–60% of cases (pilot metrics vary by insurer).

Use case 2 - Wellness incentives tied to premium benefits.

What: Policyholders receive premium discounts, cash‑back, or non‑financial rewards for meeting sustained activity or biometric targets.
Why it works: Financial and status incentives increase engagement and can shift risk exposure when widely adopted.
Design considerations: Frequency of measurement windows, prevention of gaming, and inclusion strategies for mobility‑limited customers.

Use case 3 - Early detection & claims management.

What: Continuous monitoring for arrhythmias (AF), falls, or deteriorating trends that enable early intervention and targeted case management.
Why it matters: Early detection reduces morbidity and downstream claims severity and increases customer value.
Design considerations: Clinical validation thresholds, escalation pathways, and regulatory boundaries for providing ‘health alerts’.

Use case 4 - Longevity management and annuities.

What: For insurers offering annuities, longitudinal wellness engagement data can inform product design for longevity hedging, deferred annuity pricing, and wellness‑linked riders.
Why it matters: Successful wellness programs that materially increase life expectancy can increase the longevity liability for annuity writers, necessitating new hedging and reserving practices.

Agentic AI refers to modular, goal‑oriented software agents that autonomously perform tasks (monitoring, nudging, triage, escalation) on behalf of a user or organization. In an insurer context, agentic AI can:

• Continuously profile policyholder risk using streaming data and surface only actionable summaries to human underwriters.
• Deploy personalized nudges - time‑and‑context sensitive behavioral prompts (micro‑goals) to increase adherence.
• Automate gating decisions for simple underwriting outcomes (e.g., issue at standard rates) with human oversight for edge cases.

Guardrails: each agent must operate under explicit constraints (explainability, audit trails, escalation triggers). The Geneva Association and reinsurer research emphasize that fully automated pricing, the removal of human oversight, is unproven and risky for fairness and regulatory compliance.

Evidence indicates that well‑designed incentives (small, frequent, loss‑framed, and social) produce measurable increases in activity and health markers. Key behavioral design elements:

• Small, achievable micro‑goals (reduce cognitive friction)
• Loss framing or deposit mechanisms (increases adherence)
• Social norms & competition (group challenges)
• Timely feedback (immediate rewards)
• Personalization (goals aligned to baseline fitness and constraints)

Sustaining behavior beyond incentive periods is the primary challenge; longer horizon programs that combine incentives with habit‑formation techniques (implementation intentions, contextual cues) show higher long‑term retention.

Mortality vs. Longevity Tradeoffs

Successful wellness programs reduce near‑term mortality risk (reducing claims for term and whole‑life products) but may increase long‑term life expectancy - a risk in aggregate for annuity portfolios and an influence on overall capital and reserving strategy.

Pricing and Segmentation

Wearables enable finer segmentation (behavioral phenotypes), which can improve pricing accuracy but may also provoke regulatory scrutiny concerning fairness and availability of affordable coverage.

Model Risk and Validation

Streaming data introduces non‑stationarity, seasonality, and device drift. Actuarial models must include robust back‑testing, cohort analysis, and conservative reserving approaches while hedging for anti‑selection and behavior extinction.

Key legal/regulatory considerations:

• Consent and purpose limitation: data must be collected and used only for clearly stated purposes and with informed consent.
• Data minimization and retention: insurers should keep only necessary data and apply retention policies consistent with the law.
• Regulatory frameworks: GDPR (EU), HIPAA (USA, where PHI is implicated), India’s DPDP Act (2023), and jurisdictional insurance regulator guidance must be considered. Model governance is increasingly expected by insurance regulators (examiners and industry bodies).
• Fairness & non‑discrimination: avoid proxies that indirectly encode protected characteristics; document explainability and appeals processes.
• Technical privacy patterns: pseudonymization, encryption in transit & at rest, differential privacy for cohort analytics, federated learning to avoid centralizing raw signals when acceptable.

Commercial archetypes:

• Classic insurer + rewards: retain risk pooling but use rewards to increase engagement and reduce near‑term claims.
• Insurer as wellness platform: derive revenues from subscription wellness services, device financing, and ecosystem partnerships.
• Risk‑sharing with reinsurers/partners: share upside when wellness programs demonstrably lower claims.
• Metrics of success: engagement rate (DAU/MAU), active device coverage, incremental reduction in claims frequency/severity, persistency uplift, LTV uplift, ROI vs. device subsidy costs.

• John Hancock Vitality (USA): decade‑long program linking activity to rewards and premium benefits; emphasizes sustained engagement and clinical partnerships.
• Discovery Vitality (Discovery Group): behavior‑change platform with reported reductions in admissions and measurable life‑expectancy differentials among highly engaged members.
• United Healthcare Rewards (pilot): device‑linked incentive program focused on member incentives and HSA funding.

Each demonstrates that high engagement is attainable with meaningful incentives, but scalability and equitable access remain central challenges.

Wearables convert opportunity into obligation: they give insurers new levers to reduce short‑term mortality risk and enhance customer value, but successful deployment requires new capabilities in data science, governance, and actuarial risk management. Leading practice recommendations:

1. Pilot narrowly, govern broadly: define a single measurable outcome per pilot and build strong governance before expanding.
2. Protect inclusion: design programs that accommodate device ownership gaps and mobility limitations so benefits aren’t limited to privileged cohorts.
3. Model conservatively: incorporate behavior persistence uncertainty and longevity upside into reserving and capital models.
4. Adopt privacy‑first architectures: prefer pseudonymization, federated analytics when possible, and clear consent UX.
5. Human in the loop: maintain human oversight on underwriting and pricing decisions while using agentic AI for monitoring and nudging.