Data Analytics

You need to be able to apply big data analytics in near-real or even real time while engaging with a customer or another computer.

There are three keys to remember as we start on the path to better analytics so that there are no surprises along the way.

How is your insurance company instrumented? Could you make money if you learned something in five minutes instead of five weeks?

Algorithms have been developed and are moving from a proof-of-concept phase in academia to implementations in insurance firms.

Most insurers are using some form of predictive modeling, but it can be difficult to know if it will remain effective over time.

The current approach to blockchain could mean a continued risk of siloed thinking rather than the needed cooperation.

With big data, we can better understand the causal paths between data generation and an event. There becomes no need for stereotyping.

When risks are largely independent, big data components have a computing and accuracy function to play in underwriting.