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Why Is Fraud So Hard to Detect?

Sep 21, 2017 3 MIN. READ

Agencies face a number of hurdles when it comes to detecting — and resolving — serious fraud.

Kyle Tuberson
Kyle Tuberson
Vice President
Vice President
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In 2015 alone, the Internal Revenue Service (IRS) lost an estimated $2.2 billion in fraudulent payments.The loss suggests that despite regulatory pressure to adopt better data analytics — such as the Fraud Reduction and Data Analytics Act (FRDA) — fraud is not only common, but potentially catastrophic.

The pace of technology growth and the limitations of traditional analytics frameworks mean that fraud prevention is a moving target. Fraud offenders are constantly evolving their techniques, forcing technology solutions to evolve in kind towards a big data ecosystem that is scalable, flexible, and modular — in other words "future proof." What makes fraud detection and resolution so difficult? Let’s look at five common challenges for government agencies.

  1. Too Much Data, Too Little Time: Forrester reported that leveraging big data and analytics in decision-making would be a top priority for most federal CIOs in 2016, but the volume of data makes it harder than ever to separate the signal from the noise. For example, a forensic investigation conducted by the U.S. Department of Defense (DoD) required sifting through a whopping 31 terabytes of data before spotting a violation. Aging government SQL-based databases simply can’t handle this volume of data. Infrastructure will need to shift towards NoSQL databases and other structures that can scale to meet today’s (and tomorrow’s) storage requirements.
  2. After-the-Fact Fraud Detection: Identifying fraud after the fact then trying to recoup payment has been the historical approach, and it has failed. Fraudulent behavior must be recognized in real-time so decisions can be made before payments are issued. Lighter, open-source programming languages can help achieve this goal. Batch processes must also convert to data streaming solutions for real-time analysis to be meaningful. Another enabler is machine learning, which can remove the human from the loop in identifying fraud tactics.
  3. Interoperability Issues: A slew of large government organizations, from the IRS to the Federal Bureau of Investigation (FBI) to the Office of Veterans Affairs (VA), rely on peer datasets to support their work. Many of these organizations, however, don’t speak the same language when it comes to data, making real-time information sharing especially onerous. Often transfers move in only one direction, leading to inconsistency between agencies. Legacy procedures to reconcile data—sometimes even those within the same organization—are manual and time-intensive. For instance, is the John Doe that filed his taxes with the IRS the same John Doe that applied for social security benefits or filed claims under Medicaid? The answer can be elusive unless the organizations providing these services are on the same page in terms of data.
  4. CIOs Lack Funding for IT Modernization: Fraud imposes high costs for government organizations and citizens at the best of times, but it becomes prohibitive when government leaders are already weighing significant budget tradeoffs. According to the Professional Services Council (PSC), the mere operation and maintenance of existing systems consumes roughly 80% of federal IT budgets. Continuing resolutions also limit CIOs’ abilities to invest in new technology and long-awaited legislation to modernize IT infrastructure, such as the Modernizing Government Technology (MGT) Act of 2016, has thus far failed to curry favor with lawmakers. If these hurdles persist, more CIOs may look to cloud-based data storage solutions, which will mitigate the upfront investment costs of the transition and may even reduce pressure on O&M budgets.
  5. A Widening Talent Gap: In the face of these challenges, it is becoming increasingly difficult to maintain the right expertise in-house. Budgets are flat or shrinking, headcounts declining, and technology is changing at a frantic pace. Further, the skills required are proliferating, from data scientists and domain experts to software developers and cloud engineers. Agencies need an adaptive staffing model and a flexible, scalable analytics framework.

Are these challenges consistent with the ones you and your team experience? How well are federal agencies managing fraud detection, and where could we do better? Tell us what you think on Facebook, Twitter, and LinkedIn.

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