The hefty toll of fraud
Governments turn to analytics to fight waste, fraud and abuse
Fraud follows money. No sector is immune – especially government. US government agencies operate on such a large fiscal scale that perpetrators believe their activity will go undetected. In the end, it’s the taxpayers who suffer. Fortunately, powerful data management and analytical capabilities exist today to help government agencies mitigate losses from waste, fraud and abuse.
Waste, fraud and abuse (WFA) of taxpayer dollars not only deprive citizens of critical benefits, but undermine the public faith in government programs and the principals under which they are established. The US Government Accountability Office estimated that state governments will lose $67.4 billion this year to WFA across the five largest federal benefits programs.
Medicare and Medicaid fraud
All health care programs are subject to fraud. However, as the largest programs, Medicare and Medicaid are the most visible and vulnerable. Medicaid fraud is estimated at 10.4 percent of the program’s total budget. That's $30 billion per year for both federal and state contributions. Fraudulent and unnecessary billings to health care payers are prevalent throughout the country. These activities are becoming increasingly complex and can be perpetrated by corporate-driven schemes and systematic abuse by providers and recipients.
Benefit and tax fraud
The struggling economy has taken its toll on the workers’ compensation system. With premiums declining steadily over the past few years, fraud has only exacerbated the problem. The financial integrity of the workers’ compensation system depends on employers voluntarily and accurately reporting hours worked and paying the premiums they owe. Failure to identify and take prompt action against employers who cheat the system results in higher premiums for legitimate employers.
Employees (often in collusion with providers) also defraud the workers’ comp system by faking injuries. The malingering employee gets paid for time off from work, while providers get paid for services not rendered. The ultimate result is that workers are not afforded the benefits to which they are entitled. Unemployment insurance, disability insurance and income tax are also affected.
Worker misclassification provides another opportunity for WFA. According to the best available estimates, it appears that at least 5 million workers across the US are misclassified as independent contractors, rather than as employees. This misclassification problem represents a kind of black market that operates to defeat government regulation and taxation, costing federal, state and local governments the revenues that are expected, and needed, to fund public services and programs.
Government organizations are looking to cost-effectively access and combine the wealth of employer data available. The problem is that the data resides in various data silos distributed across many systems, locations and departments. Government organizations are not hindered by a lack of data, but rather their ability to combine and analyze it.
Fighting back with analytics
The California Employment Development Department collects $31 billion in state employment taxes and disburses, on average, $6.2 billion in unemployment insurance benefits to 2.3 million claimants. The agency uses SAS to combat benefit and tax fraud, resulting in increased revenue and the identification of more collectible cases, and improving unemployment insurance fund solvency. The agency also expects to see an overall increase in program integrity through the identification of trends and emerging issues.
The Los Angeles County’s Chief Executive Office, and the Department of Public Social Services, conducted a pilot project in 2008 to test the effectiveness of analytics in detecting and preventing public assistance fraud. It achieved an 85 percent accuracy rate in detecting collusive fraud rings.
Results from the project also showed that fraud analytics could have reduced losses by nearly $7 million in areas such as new fraud referrals, early detection of fraud and increased efficiency. Furthermore, the results indicated that with additional data sources, and the use of more predictive fraud detection models, further cost avoidance can be achieved.
As state revenue bases decline around the country, more of them are turning to analytical technology to uncover billions of dollars in unpaid and underpaid tax revenues to operate more efficiently and avoid raising taxes. Additionally, with a robust audit and recover system, states can also identify those individuals and businesses that may have simply overlooked certain tax obligations.
For example, the State of Wisconsin's Department of Revenue is using SAS data management and analytics to implement an audit prioritization system, which is helping the state collect millions in tax revenue faster.
Government agencies that have implemented SAS solutions for managing WFA and improper payments have achieved notable successes. The US Office of Personnel Management uses SAS to identify bogus claims or administrative problems in health care claims for more than 9 million federal employees and their families. Officials estimate that SAS has cut analysis time in half, freeing auditors to perform other analysis.
So, while fraud takes a hefty toll on government programs, which are also subject to budget cuts and inadequate or reduced funding, government agencies at all levels have to be vigilant in identifying fraud, waste and abuse. The technology is available today to advance fraud detection, reduce losses and realize a rapid and dramatic return on investment.
Marie Lowman is an Industry Marketing Manager for the SAS Government Practice group.