Data Analytics

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.

Some insurers see fraud as just a cost of doing business, but that attitude can't continue in an era of razor-thin margins -- and there are answers.

Fine words about protecting privacy don't mask how consumers are being denied decision-making power about their own data.