Using AI to Measure Insurance Risk

At Xyonix, we use your data to build custom AI and machine learning models that can accurately and efficiently measure risk.

man walking on a tightrope holding a long metal pole

Photo by Graeme Maclean - CC

The insurance business depends on the accurate measurement of risk. Predictive models have been built for decades using sophisticated statistical models and tables of structured data. However, machine learning allows for the first time large volumes of unstructured data to be incorporated into risk models.  

Notes, reports, transcripts, social data and even imagery – data that is being produced and captured at ever increasing volumes - can now be efficiently included into risk calculus and can now contribute meaningfully to forecast accuracy.

Want to know whether those mounds of dirt from satellite images on that land are actually an unreported motocross track? Interested in using AI to better measure risk? Contact us and let’s dig in together.

Featured
Customer Behavior Analyzer

Customer Behavior Analyzer

We worked very closely with executives of a major dental insurance company to help better understand their customers. We used historic data to predict future customer behavior and purchase selections. Our models predict which customers are likely to lapse, and which are likely to remain loyal. Our models are also explainable, so it is easier to understand which attributes of a customer are indicative of loyalty or lapse tendencies. In addition to understanding high level customer characterizations, our explainable models describe why a specific customer is predicted to be a lapser or loyalist.

testimonials
Sherri Engvall

“Xyonix’s most impressive characteristic is their passion for helping us solve our problems. They truly believe in what we’re trying to achieve — they’re excited about it. Their members see the value of our work and understand our vision.”

Sherri Engvall, IT Manager @ Delta Dental of Washington [more]

Ian Kalin

“Xyonix proved instrumental in helping us formulate our AI and machine learning strategy. They asked great questions and provided deep analysis which helped us better understand our customers, business and data.”

— Ian Kalin, CTO @ eHealth, Inc. [more]