Anand Rao

Big data is getting bigger and faster. We will not be able to generate meaningful insights without advanced analytics and artificial intelligence.

As you contemplate the introduction of artificial intelligence, you should articulate what mix of three approaches works best for you.

There is a clear opportunity for prescient and active carriers to separate themselves from the pack.

Most insurers are looking outside the industry for the best ways to improve their systems, processes and products.

We are moving to a new stage of augmented intelligence, where humans and machines learn from each other and redefine what they do together.

Detractors of artificial intelligence say it hasn't live up to the big promises. In fact, AI is everywhere -- and just getting started.

While beating the best human at Go is impressive, the hoopla surrounding AI and games perpetuates the confusion about AI’s ultimate mission.

Artificial intelligence will identify, assess and underwrite emerging risks and identify new revenue sources.

There are six opportunities that incumbents need to explore to meet customer needs while improving core functions.

Here is a look at how the tipping point will play out in each key insurance sector and at how businesses can capitalize.

At the tipping point, here is a look at the five factors -- social, technological, environmental, economic and political -- that matter most.

To end up on the right side of the tipping point, insurers need to look years ahead and keep pace with a series of developments we call STEEP.

Robo-advisers are winning investment clients and have big implications for financial services firms, including insurers.

Reinventing life insurance is necessary to get the industry out of doldrums that have lasted for decades. A proven method is the "LITE" model.

Predictive analytics can be helpful but has two clear problems. Prescriptive analytics and complexity science provide the next leap forward.

To reverse the long-term decline in those buying life insurance, companies must become relevant to younger, healthier people.

There are not enough good data scientists to go around. So, should you "buy" them, "rent" them or "build" them. A hybrid may be the answer.

In essence, business intelligence needs to transcend data, structure and process and be not just a precise science but also a well-integrated art.