The insurance industry is data rich, the ability to mine the data by asking questions is antiquated and thus it tends to be knowledge poor
Mo Data stashed this in Big Data in Insurance
USING BIG DATA TO SHAPE EMPIRICAL DECISION MAKING IN INSURANCE APPLICATIONS, MURLI BULUSWAR, AIGhttp://bigdata.csail.mit.edu/node/67
"He emphasized that while the insurance industry is data rich, the ability to mine the data by asking questions is antiquated and thus it tends to be knowledge poor. Giving an example of auto-insurance, he claimed that the factors that insurance companies currently take into consideration -- age, gender, marital status, accident history, income, profession, educational background, etc. are not the core characteristics which can explain the risk; the characteristics like how does one drive, where does he drive, when does he drive, how much does he drive, what vehicle does he drive etc. explain the risk better and a pricing model based on these behaviours would be far more effective.
In U.S., the insurance pricing need to be justified by explaining all the factors (for an example, previous driving experience) and how these factors influence the risk and thus eventually justify the pricing. The insurance companies can’t create a black box model; they need to show in a transparent way, the connection between the characteristics and the pricing. This justification is possible only by mining data."
Also this from the WSJ: http://online.wsj.com/article/SB10001424052970203436904577148853410184904.html