Pricing and Rating:
Match Rates to Risk with HazardHub

Summary:

  • HazardHub delivers property-level variables that allow actuaries to replace broad territory proxies with causal features, a shift that enables more precise rate segmentation.
  • The granular data supports the refinement of rating tiers and relativities, and it facilitates portfolio diagnostics through one-way cuts to identify underperforming segments.
  • The platform provides transparent, governance-ready documentation for model validation and regulatory review, with data that offers broad reuse across underwriting and product teams. 
Better Data. Smarter Decisions.
See how HazardHub can help you assess property risk and sign up for a free trial

A Smarter Way to Achieve Rate Adequacy

Pricing actuaries know the challenge: match rates to risk without over-relying on broad proxies like territory. HazardHub delivers property-level variables and peril scores that bring causal features into your pricing models. This shift enables more accurate relativities, stronger portfolio balance, and better rate adequacy.

With HazardHub Peril Scores and Variables, you can:

  • Replace blunt proxies with causal factors like slope, elevation, and flood proximity.
  • Match high-risk properties with higher rates and reward safe properties with fairer pricing.
  • Improve rate adequacy with more predictive signals and fewer generalizations.
  • Gain portfolio insights that sharpen underwriting and pricing decisions alike.

Tools That Support Every Model Build

HazardHub provides datasets and documentation designed for modeling teams:

  • HazardHub Variable Dictionary: definitions for every variable and score.
  • HazardHub Training Sample Dataset: de-identified records for quick testing.
  • HazardHub Modeling and Governance Checklist: step-by-step guidance for stability, fairness, and lift validation.

From Proxies to Causes

Traditional rating often relies on proxies like ZIP code or territory, which miss the nuance of actual exposure. HazardHub enables a different approach—leveraging causal features tied to real-world conditions. This allows actuaries to refine relativities, improving credibility and reducing adverse selection.

For example, HazardHub Flood Variables can identify properties near flood boundaries, sharpening rating tiers or informing endorsement pricing. What once required broad generalizations is now supported by precise property-level signals.

Model Build in Practice

Adding HazardHub data to a pricing workflow is straightforward. Variables can be appended to policy and claims datasets, then tested for lift and stability. Actuaries can:

  • Run univariate scans to test predictive strength.
  • Identify useful interactions between features.
  • Validate lift while tracking drift over time.
  • Document governance artifacts with reason codes.

This process ensures every model improvement is transparent, auditable, and explainable to internal stakeholders.

Portfolio Diagnostics at Scale

HazardHub not only strengthens models, it also improves portfolio-level clarity.

The benefits include:

  • One-way cuts that reveal underperforming segments needing pricing adjustments.
  • Transparent inputs that highlight where underwriting rules should adapt.
  • Repeatable signals that allow product teams to align on strategy.

Together, these advantages ensure portfolios stay balanced, defensible, and aligned with company appetite.

Breadth of Reuse

HazardHub’s data isn’t just for actuarial teams. The same features can be applied across the organization—from underwriting playbooks and inspection rules to lead targeting and product design. This consistency reduces redundancy and ensures a single source of truth across departments.

Governance and Fairness

Pricing models are increasingly expected to demonstrate not only predictive power but also fairness and compliance with regulatory standards. HazardHub supports this by offering transparency into how variables are defined, used, and monitored over time.

Key advantages include:

  • Documented variable definitions that clarify what each feature represents.
  • Versioning and monitoring logs that track updates for audit readiness.
  • Governance artifacts that make it easier to respond to regulator or internal review requests.

This combination of predictive strength and governance-ready documentation ensures that pricing models are both effective in the market and defensible to stakeholders.

Elevate Your Pricing and Rating with HazardHub

Pricing models succeed when they combine predictive power with transparency. HazardHub makes that possible by replacing proxies with causal features, refining relativities, and providing governance-ready documentation.

Start improving rate adequacy today to see how HazardHub transforms pricing precision into competitive advantage.

 

 

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