South Carolina county using data mining to find tax dodgers

South Carolina county using data mining to find tax dodgers

on Aug 27, 14 • by Chris Bubinas • with No Comments

Charleston County recently hired a data mining company to help the local government crack down on tax dodgers...

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There are virtually no limits to the possible applications for data mining solutions. These tools have been applied to countless projects and more uses arise every day, yielding incredible insight for a huge range of purposes.

The most recent example of this trend can be found in South Carolina, where Charleston County hired a data mining company to help the local government crack down on tax dodgers

Fraud trouble
The Post and Courier reported that homeowners in Charleston County are entitled to tax breaks so long as they occupy the houses in question. A property not occupied by the owner is subject to taxes of up to three times as much as owner-occupied units, and legally no one can claim residence at more than one property.

However, many officials in the Charleston County government believe that numerous individuals falsely claim residence in rental properties and other units in order to fraudulently receive these significant tax benefits. According to officials, these properties are worth hundreds of millions of dollars, meaning that the county is missing out on a tremendous amount of revenue due to these false claims, The Post and Courier reported.

Data mining to the rescue
In order to identify those who are illegally benefiting from the South Carolina homeowner property tax reduction, Charleston County hired a data mining agency to compare data for more than 88,000 owner-occupied homes in the county against a national database.

"If the data shows (a property owner is) registered to vote somewhere, their kids go to school somewhere, their cars are registered somewhere, it all starts adding up," said Bryan Fawcett of Tax Management Associates, the organization that will conduct this investigation, according to the news source.

Government benefits
The Post and Courier noted that TMA has previously conducted similar efforts in a number of other counties, with significant results. In Dorchester County, for example, the firm identified 360 property owners from a list of 38,303 who were receiving property tax benefits inappropriately. This led to more than $800,000 in tax bills.

In these cases, the property owners in question were only forced to pay the difference between the correct property taxes and what they had actually paid for the current year and as many two year prior. In Charleston County, however, offenders will not only owe back taxes at the higher rate, but also be forced to pay interest. Additionally, these individuals will forfeit the property tax they previously paid as a penalty, the news source explained.

With its harsher penalties and greater number of homes, the financial benefits for Charleston County will likely far surpass those experienced in Dorchester.

"If this works out right, there will be a surge in revenue," said Keith Bustraan, chief financial officer for Charleston County, The Post and Courier reported. "Some of that should be permanent."

Fawcett told the news source than in every state that TMA has conducted an audit such as this, between 1 and 1.5 percent of owner-occupied properties were found to receive inaccurate tax bills. Considering the fact that Charleston County's owner-occupied homes are valued at more than $26 billion, this would amount to between $261 million and $391 million in property that is being taxed incorrectly, leading to millions of dollars of additional revenue.

Reducing costs
As these numbers highlight, data mining may prove an extremely valuable resource for Charleston County's government, thanks to the discovery of previously overlooked revenue potential. But data mining efforts can improve governments' ledgers by cutting costs, as well.

For example, some hospital systems now apply data mining tools to patients' credit card information as a means of accurately predicting who is in danger of becoming sick in the near future. This way, doctors and nurses can take preventative measures and encourage healthier behavior on an individual level. In the context of Medicare, Medicaid and other government-related health care programs, these efforts could drastically cut costs, as a trip to the emergency room is far more expensive than a proactive checkup.

When applied in a wider context, there is no limit to the savings that data mining efforts may provide.

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