Author: Ron Hodge, Jeff Butler, Michael Dunn, IRS
The mission of tax administration includes providing service to assist taxpayers with their tax-related filing, reporting, and payment obligations; enforcing tax laws; and adjudicating any tax issues. Taxpayers have an expectation that these activities are administered in a fair and impartial manner with effective stewardship of tax dollars. To deliver timely and accurate service, multiple channels are needed to meet a diverse set of service preferences, including phone, in-person, internet, and written correspondence. Optimizing service delivery objectives across channels involves balancing the demand for service against internal capacity constraints, and through external partnerships with tax preparers, financial institutions, volunteer groups, and other stakeholders. Enforcing tax laws, whether civil or criminal, requires identifying compliance risk in areas that include ID theft, refund fraud, multi-party tax schemes, off-shore transactions, and money laundering. It requires optimizing workload decisions across business processes, often interconnected, in examination, collection, and criminal investigation domains, to achieve the best set of outcomes for both IRS and taxpayers. To address these challenges, modern tax administration has an opportunity to leverage state-of-the-art analytics in areas such as machine learning, deep learning, natural language processing, graph mining, and simulation to improve decision-making and meet mission objectives. This tutorial will highlight how the Internal Revenue Service is using these capabilities to transform operations and create a more efficient and cost-effective tax administration.