National Stop-Loss Carrier (unnamed)

Stop-loss carriers underwriting group health coverage face a structural blind spot: traditional actuarial inputs — historical claims, census data, and standard risk factors — cannot reliably distinguish between groups with stable, predictable claim patterns and those carrying concentrated catastrophic exposure. A single high-volatility employer group can generate losses that dwarf collected premiums, yet nothing in the conventional underwriting process flags it before binding. For one national stop-loss carrier, this gap materialized across a portfolio of 19 employer groups totaling 16,823 members, where adverse selection eroded underwriting margins and produced a combined medical loss ratio of 83.6% — leaving profit margins at just 16.4%.

Merit Medicine's Merit Predict platform was applied retrospectively to the carrier's bound portfolio to evaluate what AI-led pre-bind risk stratification would have produced. The platform combines predictive machine learning, clinical intelligence, and actuarial benchmarking to generate two group-level scores: the Merit Stop Loss Risk Score (measuring claim volatility and future predictability) and the Merit Aggregate Risk Score (comparing first-dollar risk against national benchmarks). Beyond group scoring, Merit Predict surfaces member-level health status summaries, primary diagnoses, high-cost drug utilization, and predicted medical and pharmacy spend. Groups are stratified into five tiers — Tier 1 (lowest risk) through Tier 5 (highest risk) — enabling underwriters to price, exclude, or reprice specific groups before any coverage is bound. The methodology was independently reviewed and validated by actuarial firm Axene Health Partners.

Applied retrospectively to the same 19-group portfolio, Merit Predict correctly flagged four of the six groups that ultimately generated material losses, classifying them as Tier 5 prior to any underwriting decision. The financial impact of acting on those signals would have been substantial:

• Medical loss ratio: reduced from 83.6% to 55% — a 29-percentage-point improvement
• Underwriting profit margin: increased from 16.4% to 45% — a 107% improvement
• Return on investment: projected at 6.9X, independently validated by Axene Health Partners
• Four Tier 5 groups (21% of total) accounted for 52% of all underwriting losses
• The single largest loss — $2.3M — came from the group with the highest Stop-Loss Risk Score, 1.5X above any other group in the study

• Loss concentration is the core stop-loss risk: a small fraction of groups (21% here) can drive the majority of portfolio losses, making pre-bind stratification more valuable than broad pricing adjustments.
• AI-based risk scoring enables a two-sided strategy — not just avoiding Tier 5 groups, but pricing more aggressively on Tier 1–3 risks to improve win rates without increasing capital exposure.
• Retrospective validation against real bound portfolios provides a credible proof-of-concept before committing to full deployment; carriers should demand this before adopting any new risk model.
• Independent actuarial validation is essential for regulatory defensibility and carrier buy-in — methodology auditability matters as much as predictive accuracy.