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.

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).

Insurance CIOs and CUOs historically have opted to build from scratch, and it’s hard to blame them. Underwriting organizations differentiate their offering in the market through unique knowledge, processes and rating methodologies. These practices inevitably generate unique data and dependencies. That unique data, while critically important, makes up a small fraction of the total volume of data an organization needs to manage.

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