Real-Time Insurance Pricing Transformation With Rocket^® Vertica^® 

Company

Industry: Insurance

This organization is a leading European insurance provider operating across multiple markets. With thousands of employees and an extensive distribution network, it serves customers who expect fast, personalized, and competitively priced coverage.

Challenge

Real-time, personalized pricing required analyzing thousands of variables instantly, but heritage tools were too slow and rigid to keep up.

The pricing team was responsible for updating customer data, running segmentation analysis, and managing ongoing and ad hoc reporting, all while speed and accuracy were non-negotiable. As competition intensified, personalized quotes demanded real-time analysis of thousands of variables, since yesterday's price often didn't reflect today's market. The organization's existing technology couldn't keep pace, limiting experimentation, automation, and the team's ability to adapt pricing to shifting conditions and customer demographics.

Solution

After an extensive proof-of-concept process, the organization chose Rocket Vertica for its speed, scalability, and automation capabilities.

Rocket Vertica delivers lightning-fast query performance across large, complex datasets. Its in-database machine learning and AI capabilities allow predictive models to run directly within the platform, removing the latency of moving data elsewhere, and feeding insights straight into the pricing process. The implementation also modernized existing workflows: manual file imports gave way to near-real-time data ingestion, and Rocket Vertica's projection capabilities eliminated manual query optimization. Analysts gained the freedom to focus on insights instead of technical overhead, supporting both scheduled reporting and dynamic, on-the-fly analysis.

Results

Rocket Vertica delivered immediate, measurable impact: faster queries, sharper risk analysis, and a rapid return on investment.

Query performance improved 25 times over, allowing analysts to test more hypotheses at a scale that was previously out of reach. Sharper predictive analytics helped the team identify higher-risk policyholders more accurately, contributing to a five percent reduction in claims frequency across the portfolio, a meaningful figure given its size. The organization reached full ROI within a year. Dynamic, on-demand reporting also changed the rhythm of decision-making, with leadership now able to adjust reports live during meetings instead of waiting on post-meeting analysis. The result is a more agile pricing function with a stronger competitive edge.

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