AI Underwriting: How Technology Is Changing Stop-Loss for Groups Without Claims Data

# AI Underwriting: How Technology Is Changing Stop-Loss for Groups Without Claims Data

Stop-loss underwriting traditionally relies on two pillars: actuarial manual rates and historical claims experience. But what happens when one of those pillars—claims experience—is missing?

## The Challenge of No Claims History

This scenario is common for:
- Small groups transitioning from fully insured coverage
- Start-ups entering the self-funded market
- Groups with limited or incomplete data
- Employers changing TPAs without data portability

Historically, carriers addressed this gap with:
- Conservative pricing (high percent-to-manual)
- Additional risk loads
- Individual Health Questionnaires (IHQs)
- Longer quote cycles
- Sometimes, declinations

These measures protect carriers but often result in higher premiums and friction, making self-funding less attractive for smaller employers.

## AI Changes the Equation

AI-driven underwriting platforms offer an alternative by predicting risk using **non-traditional data sources** and advanced modeling techniques.

Instead of relying solely on historical claims, these tools analyze:

### Medical and Pharmacy Data
- Eligibility feeds
- Rx histories
- Lab results
- Diagnosis codes

### Behavioral and Lifestyle Indicators
- Social determinants of health (SDoH)
- Engagement patterns
- Demographic attributes
- Geographic risk factors

### Population Health Signals
- Predictive markers for chronic conditions
- High-cost therapy probability
- Hospitalization risk scores

## Leading AI Platforms in Stop-Loss

Two platforms dominate this space:

### [Gradient AI](https://www.gradientai.com/) (SAIL)

Specializes in medical, Rx, and lab data to forecast large-claim risk:
- Models refresh monthly
- High match rates for accuracy
- Ideal for MGUs and carriers quoting small groups
- Provides group-specific risk scores
- Flags high-dollar Rx and medical conditions

### [Verikai](https://www.verikai.com/) (CaptureHealth & Capture360)

Expands beyond clinical data to behavioral and lifestyle factors:
- Models social determinants of health
- Predicts utilization trends claims history can't reveal
- Particularly useful for groups with no prior self-funded experience
- Consumer behavior analytics integration

Both platforms integrate with underwriting workflows, reducing reliance on IHQs and accelerating quote turnaround.

## Benefits of AI Underwriting

| Benefit | Impact |
|---------|--------|
| **Competitive Pricing** | Reduces need for conservative manual-heavy quotes |
| **Faster Quoting** | Cuts cycle times from weeks to days |
| **Better Risk Segmentation** | Identifies favorable risks in thin-data markets |
| **Reduced Friction** | Fewer IHQs and paperwork |
| **Market Access** | Makes self-funding viable for smaller groups |

## Strategic Implications for Brokers

Understanding which carriers leverage AI underwriting is critical when marketing groups without claims experience:

- **Ask carriers** about their AI/predictive tools
- **Identify AI-enabled MGUs** for small group placements
- **Position clients strategically** based on available data
- **Set expectations** about what data helps vs. hinders

## The Future of Stop-Loss Underwriting

As adoption grows, AI underwriting will become a key differentiator in the stop-loss market—reshaping how carriers approach risk and how brokers position their clients.

The carriers that embrace these tools will capture market share in the small and mid-sized group segment, while brokers who understand the landscape can better serve clients previously locked out of self-funding.

*This article is part of our series on stop-loss underwriting. For a comprehensive overview, see our whitepaper: [Stop Loss Underwriting: A Deep Dive](/resources/whitepapers/stop-loss-underwriting-deep-dive).*