Q: How would you characterize your agency’s current and/or planned use of predictive analytics? What is one specific way in which predictive analytics actively drives decisions in your agency?
A: The IRS uses a wide range of analytic methods, tools, and technologies to address such problems as ID theft, refund fraud, inventory optimization, and other activities related to its statutory mandates. In an era of persistently reduced budgets, the use of data analytics has become more important than ever to drive innovation, risk management, and decision making across the agency.
Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?
A: Large organizations don’t change their leopard spots overnight. Building a data-driven culture involves fundamental changes to workforce skills and business-IT relationships, which requires change leadership and long-term commitments.
Q: Can you discuss any near term goals you have for improving your agency’s use of predictive analytics?
A: The U.S. taxpayer population has some complexities that present unique challenges to the IRS. For example, high-wealth individuals often behave more like a business, and businesses with connected entities often look more a group of interrelated economic structures than a single business. There is growing interest in network analysis and related methods as an exploratory approach to better understand these types of patterns.
Q: Can you describe a successful result from the employment of predictive analytics in your agency, i.e., cost avoidance, funds recovered, improved efficiency, etc.
A: ID theft remains a significant challenge for the IRS—and therefore for U.S. taxpayers as well. The financial and psychological cost to families whose tax returns are fabricated by ID thieves can be devastating and long lasting. The use of data analytics has allowed the IRS to accelerate the process of verifying ID theft cases for faster case resolution, lowering direct costs through improved automation. Analytic models are also key to detecting and preventing billions of dollars in fraudulent refund claims each year.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.
A: Greater awareness is needed by agencies that the traditional paradigm for analyzing data in massively large environments is changing and skills need to adapt. Organizational boundaries between IT and business have to be removed. Greater emphasis needs to be placed on multi-disciplinary teams that combine skills from computer science, IT, statistics, economics, and applied math.
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