Data Mining Helps Uncover Fraud in Disaster Relief
Wednesday, June 28th, 2006 | Justin Bugajski
This recent article about the GAO’s investigation into fraudulent use of government assistance following Hurricane’s Katrina and Rita illustrates why it is becoming increasingly important to develop data analysis techniques that are proactive in real-time instead of reactive in the wrong time. What could have been done differently in this situation to help the government avoid paying out on bogus claims? While certainly there is a balance to be maintained between the need to distribute aid quickly and the need to have thorough checks into a person’s identity, surely there must be a better way to manage the situation than what was done by the government after these two disasters occurred.
A government watchdog relied on data mining to uncover an estimated $1 billion of improper or fraudulent payments for assistance in the aftermath of hurricanes Katrina and Rita last year.
The Government Accountability Office reported its findings on the fraud to the House Homeland Security Investigations Subcommittee on Wednesday. GAO found that the lack of “upfront controls” and inadequate data checks at the Federal Emergency Management Agency led to the improper disbursement of anywhere from $600 million to $1.4 billion to alleged hurricane victims who registered for federal assistance.
In one case, an individual using 13 different Social Security numbers, one of which belonged to the person, received 26 payments totaling $139,000. By searching public records, GAO found that of the 13 addresses that person claimed as damaged property, eight were bogus addresses or were publicly owned.
Matching information from FEMA registrations to a database of federal and state prison inmates, furthermore, GAO found more than 1,000 registrants used names and Social Security numbers belonging to prisoners who were not displaced by the storms. In one case, a Louisiana inmate received more than $20,000 for registering a post-office box as damaged property.
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