Data Analytics

Demand for commercial insurance is on the rise, but profitability remains elusive. Algorithmic data is the key to greater, granular insight into risks and prices.

By improving weather modeling and assessing past catastrophes, insurers can use predictive analytics to provide better support to customers during difficult times. 

Understanding your dark data can reveal insights into customers and employees, the quality of your assets and manufacturing and the risks your brand faces on social media.

Although the life insurance industry has been cautious by nature, data and technology are shifting the analysis of risk and enabling prudent underwriting without volatility.

Employee benefits carriers can differentiate themselves and shield themselves from disruption by harnessing predictive models to optimize pricing and radically improve profitability.

Off-the-shelf technology packages rarely produce exactly what an organization needs; with low-code, they can configure solutions to their unique needs.

Using a logical data fabric, insurance companies can apply a modern, agile approach that also allows them to streamline their data governance efforts.

Demand for data in today’s insurance companies is outpacing supply, and, in most cases, the throttle is organizational rather than technical.