Big Data And The Balance of Information Between Insurers And Consumers

The widespread digitization of the economy has created a wealth of data on risk exposures for insurance companies. But how insurers exploit what has become known as big data has prompted widespread discussion around data ethics, especially in regard to transparency and fairness where consumer data is used to market and to price risk.

Insurers are already familiar with the concept of adverse selection. Now they are getting to grips with the new concept of inverse selection that arises with big data.

It’s useful to look at a basic risk spectrum to explore how big data can fundamentally change the information balance of insurers and insureds, and the latter’s perception of risk.

Usually, the consumer has private information about where s/he is located on the risk spectrum, exposing the insurer to the risk of adverse selection. Using big data combined with machine learning, the insurer might be able to uncover the location and eliminate the information asymmetry.

Where the consumer’s risk perception is poorly calibrated, the insurer might be able to reverse the information asymmetry in its own favor by having a more precise read of the risk than the consumer has, and even determine the consumer’s perceived location.