Fenris Blog

When evaluating and building API integrations to support products like insurance, it can be difficult to manage sensitive personal information from new vendors. We are pleased to announce the availability of a synthetic dataset designed to help qualified developers easily and…

The financial details of small businesses have always been information insurers want to consider when quoting a new policy, but short of relying of self-reported data, that information could only be found in a few places. At Fenris, we are always…

We sat down with Fenris’s Chief Technology Officer, Jay Bourland, for a deep dive discussion about how the Fenris machine learning (ML) platform delivers predictive lead scoring for the insurance industry—tuned to each customer’s individual needs—faster and at a much lower…

At Fenris, we use AWS Lambdas to support our Machine Learning and Inference pipelines. Specifically, we build many of our APIs on inference models that score leads for our customers. With the sunsetting of Python 3.6, we recently had to update…

The term “tire kickers” originated in automobile sales. You found out who they were when they walked off the lot without a new car. There are tire kickers browsing online for insurance quotes as well. You can spot them by the…

COPE is the acronym for the four categories of data that insurers use to assess the risks associated with a particular property: Construction, Occupancy, Protection, and Exposure. The Fenris Home/Property Insurance Prefill API provides accurate, multi-sourced data for each COPE category.…

NAICS codes are very important to our insurance customers. It’s a critical field in applications for small business commercial insurance — but many applicants don’t know offhand which code applies to their own businesses. That’s why our customers look to our…