Case Studies

Machine Learning Modeling for Better, Faster, Cheaper Insurance Underwriting

There is a lot of value locked up in past policy records. Data scientists can locate trends, inefficiencies and loads of other insights with access to large data sets.

That data though is often trapped on actual paper, or scanned versions of it. Reading and extracting the amounts scientists need is both arduous and expensive.

At least, for human beings.

This one-page case study highlights how Chorus Document Automation helped a Top 5 US insurer crunch through over 56 million pieces of paper across a million policies to save $10 mm a year.

Download to learn more.

Machine Learning Modeling for Better, Faster, Cheaper Insurance Underwriting

Download the case study