Insurers and brokers are developing unique tools to help insureds stay ahead of global political risks. 

The private insurance market needs to better understand exposure to flooding.

Models analyze variables at intake and afterward to identify claims that may become adverse.

Complex supply chain risks are prime territory for analytics to close coverage gaps, root out weak or risky suppliers, and ensure business continuity.

With the rise of the Internet of Things, wearable tech and “people analytics” could put you out of a job.

More companies are harnessing industry data to cut down claim duration and overall cost.

A combination of technology and data-centric risk management almost guarantees resilient supply chains.

Expanding powers of analytics are fine and good. But without the wisdom of experienced risk managers, they’re not much good at all.

Simply having lots of data gets us nowhere. We need a different approach.

The location of suppliers should be a key consideration in supply chain risk management.

Modeling will make the market, experts at a New York industry event said.

The Texas flooding is a prime opportunity for insurers to learn more about automobile technology’s impact on claims numbers.

It’s important to set up environments where experimentation is possible.

The lack of accurate data, claim complexity and other factors are preventing predictive modeling from being effective for cyber risk. 

The data we collect is more powerful than ever before, but we still need to learn how to leverage it into information that can be acted upon.

Liberty Mutual’s new medical director says experience plus the rights tools equal superior claims outcomes.

Sometimes the best data you have is your own.

It will be a wild ride for the primary and excess GL market in 2015.

Experts see tremendous potential in using predictive analytics for to predict the probability of injury and disease.

Being a prompt payer insures against insolvency supply chain risk.