Analytic Objects Are the Next Evolution of Auto Risk Segmentation

June 3, 2025 / 2 min read

This is the second post in a series sharing insights from Verisk’s white paper, Auto Policy History Analytics: The Future of Risk Segmentation. Amid record-setting shopping behavior with few signs of slowing, the white paper features analysis and innovations to unlock predictive policy insights at Rate Call 1 to drive profitable, sustainable growth.

It takes holistic customer data, laser-focused segmentation, and actionable analytics, delivered seamlessly at the point of quote, to modernize insurance buying while confidently assessing risk and growing profitably. Analyzing complex historical information for rapid consumption often demands deep data and a major IT commitment for insurers to tackle it in-house.

How can personal auto insurers tap into policy history analytics to bypass internal and industry roadblocks, modernize the buying experience, and boost competitiveness? Leverage cutting-edge analytics derived from the building blocks available today.

A new standard for auto policy history solutions

Verisk’s Coverage Verifier (CV) starts with extensive data and a detailed, chronological view of past policies. CV is a comprehensive, data-forward solution that can help auto insurers segment risks using traditional analytics, such as proof of existing insurance.

Verisk’s proprietary data and reporting structures challenge the status quo by delivering policy changes, contributed by a majority of the industry, for up to seven years. This data provides unique insights into the applicant’s transaction history that aren’t available from a snapshot of their most recent policy; examples include endorsement and policy change behaviors, risk tolerance, payment patterns, and retention.

CV-data-graphic

But there’s still more insights to extract from CV data: analytic objects that enable insurers to move beyond the present. For example, what if you could spot applicants who routinely lower liability limits after issue? Industry leaders are discovering a better way to help drive profitability and refine segmentation by leveraging deep data and deeper insights at Rate Call 1, the initial quote a customer receives.

Greater than the building blocks: Analytic objects are the next evolution of auto risk segmentation

Coverage Verifier Analytic Objects (CVAO) generate predictive insights, each through a unique lens, to assemble a clear picture. Each policy history analytic object is designed to capture new risk variables or indicators and their underlying context to deliver upfront insights.

A recent Verisk study revealed that certain policy history analytic object attributes correlate with loss cost relativities up to three times higher than average, while others indicate significantly lower risk³, suggesting potential discounts rather than surcharges. The relative risk of applicants emerges through powerful policy history analytic objects within key insight domains:

risk-segmentation-graphic

These unique lenses, focusing on different angles, and the proprietary structure behind the analytics, yield deeper, more meaningful insights that can be harnessed in multiple ways.

Read the previous post in this series: “Leveraging Auto Policy Analytics to Stay Ahead of the Competition”

Take a deeper dive into auto policy analytic strategies when you read our white paper.

Download the white paper

1. Verisk, Auto Policy History Analytics: The Future of Risk Segmentation, March 2025, <https://www.verisk.com/resources/campaigns/auto-policy-history-analytics-the-future-of-risk-segmentation/ >, accessed on June 3, 2025.
2. Ibid.
3. Ibid.
4. Ibid.
5. Ibid.
6. Ibid.
7. Ibid.