The Washington Education Association (WEA, in Washington State) is partnering with Aon Hewitts (Illinois), a verification company, to eliminate a specific type of health insurance fraud: teachers reporting non-qualifying people as dependents, such as an unemployed friend with no health insurance. The fraud is used by "nice" people (teachers) to provide health insurance to people who would otherwise have none, by reporting them as spouse or kids.
Interestingly, I saw the letter sent to all WEA teachers. It requires you to fill lots of paperwork and provide multiple identity proofs (tax forms, birth certificates, marriage certificates etc.) similar to ID documents (I9 form) requested to be allowed to work for a company.
It is easy to cheat on the requested paper documentation that you have to mail to the verification company (e.g. by producing fake birth certificates or claiming you don't have one, etc). In addition, asking people to fill so much paperwork is a waste of time and natural resources (trees used to produce paper), and results in lots of errors, privacy issues and ID theft risk, and costs lots of money to WEA.
So why don't they use modern methods to detect fraud: data mining techniques to detect suspicious SSN's, identifying SSN's reported as dependent by multiple households based on IRS tax data, SSN's not showing up in any tax forms submitted to the IRS, address mismatch detection, etc. (note that a 5-day old baby probably has no record in the IRS database, yet he is eligible as a dependent for tax or health insurance purposes).
Why not use data mining technology, instead of paper - with all the advantages that data mining offers over paper? What advantages does paper offer? I don't see any.
Here's how data collection and processing should have been performed:
This should eliminate most of the fraud, at a very low cost, and with very little burden on teachers.