Traditionally, insurers have relied heavily on data they have collected as well as industry-specific data to inform their business decisions and strategy. However, data science techniques have become more sophisticated, allowing insurers to better understand the relationship between internal and external data sources. Predictive analytics, machine learning, data mining, and artificial intelligence are helping companies extract value from both sources.
In this article, Milliman’s Cormac Gleeson and Eamon Comerford discuss how the use of external data can complement a company’s wider data science initiatives. They also explore some of the challenges posed by working with external data.