Explore the application of machine learning in the pet insurance industry through this 26-minute conference talk from ODSC WEST 2015. Learn how TJ Houk and David Jaw introduced predictive modeling to their company, focusing on customer churn analysis and claims projection. Discover the process of setting up problems, generating datasets using R, building models with DataRobot, and effectively communicating results through Excel. Gain insights into the challenges of working with uncharted insurance risk territory and the importance of clear communication when introducing new analytical concepts. Follow their journey from data warehousing and model validation to risk assessment and key takeaways, understanding how machine learning can significantly impact an insurance company's operations and customer value.
Introducing Machine Learning to a Pet Insurance Company
Overview
Syllabus
Introduction
Pet Insurance Industry
Analytics Team
Team Members
Customer churn
Data warehouse
Pet per unit time
Model validation
Data robot interface
Risk vs payout
Example results
Customer churn model
Key takeaways
Taught by
Open Data Science
