Insurance Underwriting
ML-driven underwriting decisioning for MGAs and insurtechs
Insurance Underwriting API replaces spreadsheet-based risk assessment with a machine learning decisioning engine. Feed it application data, loss history, and third-party enrichment signals; receive a risk score, premium recommendation, and bindable quote within seconds. Supports property, casualty, and specialty lines.
MRR
$14,320
+12% this month
Active
487
+23 this month
Churn
1.8%
-0.4% this month
$14K/mo
Verified revenue
73%
Choose annual
98.7%
Uptime SLA
<2min
Setup time
The Problem
Sound familiar?
Manual underwriting takes days and creates inconsistent decisions across underwriters
Building proprietary loss models requires expensive actuarial talent and years of data
Spreadsheet-based risk scoring cannot scale with high application volumes
The Solution
Insurance Underwriting fixes this.
Risk Scoring Engine
Ensemble model combining gradient boosting and logistic regression, trained on industry loss datasets. Outputs risk score, confidence interval, and top contributing factors.
Third-Party Data Enrichment
Automatically pulls credit bureau, property records, weather risk, litigation history, and business registry data to enrich raw application submissions.
Premium Recommendation
Given a risk score and your rate table configuration, the engine outputs a recommended base premium and optional loading factors for unusual exposures.
How It Works
Set up in under 2 minutes. No complex configuration.
Risk Scoring Engine
Ensemble model combining gradient boosting and logistic regression, trained on industry loss datasets. Outputs risk score, confidence interval, and top contributing factors.
Third-Party Data Enrichment
Automatically pulls credit bureau, property records, weather risk, litigation history, and business registry data to enrich raw application submissions.
Premium Recommendation
Given a risk score and your rate table configuration, the engine outputs a recommended base premium and optional loading factors for unusual exposures.
Rules Overlay
Compliance team can layer underwriting guidelines on top of the ML score — mandatory declinations, capacity limits, and exclusion triggers enforced before a quote is returned.
Audit & Regulatory Reporting
Every decision is logged with input features, model version, and output reasoning. Pre-built reports satisfy state DOI audit requests and NAIC data call requirements.
Why not the alternatives?
Same result. A fraction of the price.
| Product | Price | Core feature |
|---|---|---|
| Insurance Underwriting | $300/mo | ML-driven underwriting decisioning for MGAs and insurtechs |
| Enterprise tool | $149/mo | Overkill for most teams |
| DIY approach | 40+ hrs dev | High maintenance burden |
Integrates with your stack
Simple, Transparent Pricing
No per-user fees. No hidden costs. Cancel anytime.
MGA
- 500 decisions/mo
- Risk scoring engine
- Premium recommendation
- Audit log
- REST API
Carrier
- 5,000 decisions/mo
- Third-party enrichment
- Custom rules overlay
- Regulatory reporting
- Model retraining on your data
- SLA 99.9%
Frequently Asked Questions
Which lines of business are supported?
Property, general liability, professional liability (E&O/D&O), commercial auto, and workers' comp. Specialty lines available on custom plans.
Can we retrain the model on our own loss data?
Yes, on the Carrier plan. Upload a minimum of 3 years of labelled loss history and we deliver a fine-tuned model within 10 business days.
Is the API output admissible for state regulatory filings?
The audit log and decision explanation output are designed to satisfy typical state DOI requirements. We recommend legal review for your specific jurisdictions.
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