Insurance Claims: Mining Big Data to Connect the Dots
Mo Data stashed this in Big Data in Insurance
1. Unstructured Data - lots more around, more difficult to analyze than structured but will lead to interesting insights
2. Granular Data - more granular data than ever before, however, this creates an analysis paralysis risk
3. Cost - not simply technology, this biggest costs are in resources and time
4. Privacy - this cannot be ignored in this day and age, where there is insufficient legislation and precedent.
"Insurers should ask themselves if they are merely becoming data-heavy (meaning they just have a lot of new information but may not be quite sure what to make of it all) or, preferably, if they are growing data-rich (meaning they have new information from which they can generate deeper correlations raising red flags about potential frauds). In their pursuit of big data, however, insurers should be careful about reaching a point of diminishing returns if they data-mine so widely and deeply that they end up drowning in information but starving for actionable insights."
The concept of a data-rich insurer is fascinating. I have my doubts they do anything that sophisticated.
Where there's data - there's money: http://lutzfinger.com/blog/hype-million-usd-salary/. I worked for an insurance company in the UK, they had an ocean of data... briney and not a drop to drink. But they were doing some pretty neat stuff with it.