Data Analytics

Many companies aim to be data-driven, shifting decisions away from human actors and trusting the algorithms. Instead, firms should be "data-informed." 

It would be impossible for insurers with thousands or tens of thousands of customers to fully understand each of them individually.

Most property risk models rely heavily on ZIP code. Yet, technology and data exist today to evaluate more than 1,000 risk data points for every single property in the U.S.

By removing data silos and creating a unified, contextual understanding, each department will gain a more complete picture of how best to improve overall performance.

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.