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The Insurance Data Paradox: Structure Creates Flexibility
At most insurance companies, especially the largest and oldest, data is extremely disorganized. Many carriers have five or more policy admin systems due to acquisitions. Critical data is stored in legacy mainframe databases without an API and may be accessible only via a database string. The schemas of core datasets use column names like “field A,” “field B” and “field C” rather than easy-to-understand column names.
Executive Summary
To enable flexibility in the integration and organization of insurance data, a basic level of structure is paradoxically required, writes William Steenbergen, co-founder of Federato, creator of the RiskOps platform, an AI-based platform that provides underwriters with a unified view of all the disparate information they need to select and price risks. Executive Summary To enable flexibility in the integration and organization of insurance data, a basic level of structure is paradoxically required, writes William Steenbergen, co-founder of Federato, creator of the RiskOps platform, an AI-based platform that provides underwriters with a unified view of all the disparate information they need to select and price risks.
Innovating on this disorganized foundation is nearly impossible. But it’s also urgently necessary. There are two paths out of this chaos: building a custom data architecture entirely from scratch or building on top of an “opinionated” data model provided by a technical partner (described below).
