HazardHub for Actuaries:
From Proxies to Precision

A Smarter Way to Price Risk

Actuaries know that better inputs mean better models. HazardHub brings property-level data: peril scores, exposures, and variables that slot directly into pricing frameworks. Instead of relying on broad territory proxies, you gain features tied to actual causes of loss. That shift drives more accurate rates, stronger segmentation, and clearer portfolio insight. 

With HazardHub Variables and Peril Scores, actuaries can:

• Replace ZIP or territory proxies with causal features like slope, elevation, or distance to flood boundaries.
• Run sensitivity tests and one-way cuts to identify underperforming segments.
• Improve rate adequacy by lifting predictive power, even if only a handful of variables prove significant.
• Reuse the same dataset for pricing, underwriting prefill, and portfolio diagnostics.

What Actuaries Get in Plain Terms

HazardHub delivers third-party data plus predefined models, ready to integrate. Actuaries can ingest the dataset alongside policy and claims history, testing which variables add lift in their models. The approach is practical: add many variables, expect a few to outperform, and still gain an edge in pricing.

Tools That Support Every Model

HazardHub equips actuaries with datasets and documentation designed for immediate evaluation:

• HazardHub Variable Dictionary: definitions for every variable and score.
• HazardHub Sample Dataset (CSV): de-identified pilot data for quick testing.
• HazardHub API Docs: combined endpoints to call property-level features at scale.

From Proxies to Causes

Traditional actuarial methods often rely on blunt proxies like territory. HazardHub enables a different approach, introducing underlying exposures and weather-driven features. Instead of treating two adjacent ZIP codes as homogenous, actuaries can differentiate based on the real drivers of risk.

One actionable example: use HazardHub Flood Variables to measure distance to the nearest flood boundary. This detail sharpens pricing for flood endorsements, helps refine tiers, and reduces adverse selection.

Where Speed Fits

HazardHub’s A-to-F peril segmentation offers a quick way to stratify portfolios. These buckets can guide preliminary risk grouping, model diagnostics, and downstream uses like underwriting rules or inspection triggers. Speed and depth work together: quick signals help triage, while deeper variables support robust model development.

Portfolio Diagnostics at Scale

Once HazardHub variables are integrated, actuaries can quickly diagnose how a portfolio is performing. Instead of relying on broad averages, they gain visibility into which factors are driving loss experience and where adjustments are needed.

The advantages are clear:

• One-way analyses reveal underperforming cells across the book of business.
• Sensitivity testing highlights which variables add the most predictive lift.
• Transparent inputs make it easy to explain why model changes improve outcomes.

This approach ensures every decision is grounded in data that is not only predictive, but also explainable to stakeholders.

Breadth of Reuse

HazardHub data isn’t just for pricing models. The same features support underwriting playbooks, prefill automation, inspection rules, and even lead targeting for product teams. By reusing a single dataset, organizations gain efficiency while maintaining consistency across departments.

Elevate Your Pricing Models with HazardHub

Actuaries succeed when their models balance credibility, precision, and speed. HazardHub helps make that possible by replacing proxies with causal features and delivering transparency into what drives risk.

Start integrating HazardHub into your pricing workflow today to see how property-level intelligence powers better rates and portfolio performance.