Insurance Data Quality

Every AI project in insurance eventually hits the same wall: the data isn't clean enough to trust.

Underwriting models trained on incomplete loss histories produce skewed risk scores. Claims severity models fed by inconsistent damage codes generate unreliable reserves. The math is sophisticated; the inputs aren't.

This isn't a technology problem — it's a data governance problem that most organizations have deferred for years. AI initiatives are simply exposing it faster and more visibly than anything before.

The carriers making real progress on AI are the ones investing as heavily in data pipelines, taxonomy standardization, and source-of-truth architecture as they are in model development.

Building the model is the easy part. Building the data infrastructure that makes the model trustworthy is the actual work.