Tax practitioners will face new questions from examination teams as the IRS selects compliance risks based on data, in the Large Business and International Division's (LB&I) move from individual audits of multinationals to broader considerations involving risk assessment.
- Directors of Field Operations (DFO) and
- Territory Managers (TM) constituting the management structure within the practice area.
The other five practice areas (or “PAs”) are delineated along technical or subject matter lines:
- Pass-through entities
- Enterprise activities
- Cross-border activities
- Withholding and international individual compliance
- Treaty and transfer pricing operations
Similarly, it is envisioned that all of the International Examiners will likely be housed within at least two of the international practice areas. It appears that at a strategic level, the subject matter practice areas will lead the development and coordination of the campaigns and treatments aimed at specific compliance risks. At a tactical level, the design calls for them to also work directly with examiners from the geographic practice areas that are assigned specific issues to work.
- Flexible, well-trained workforce—developing deeper specialized knowledge and dynamic tools
- Selection of better work—based on data analytics and real-time examiner feedback
- Tailored treatments—a more flexible stream of options to address current and emerging issues
- Integrated feedback loop—continual collection and analysis of data that permits more precise focus on the right compliance risks
Texas and several other states, as well as tax agencies in the United Kingdom and Australia, rely on data mining to help find delinquent taxpayers and make effective resource allocation decisions. Data mining leverages specialized data warehousing systems that integrate internal and external data sources to enable a variety of applications, from trend analysis to non-compliance detection and revenue forecasting, that help agencies answer questions such as:
- How should we split auditing resources among tax types?
- Which taxpayers are higher audit priorities?
- What is the expected yield from a particular audit type?
- Which SIC codes are associated with higher rates of noncompliance?
Tax agencies have access to enormous amounts of taxpayer data. Most auditing agencies, in fact, draw information from these data sources to support auditing functions. Audit selectors, for example, search data sources for taxpayers with specific profiles. These profiles, developed by experts, may be based on a single attribute, such the taxpayer industry code (SIC), or on a complex combination of attributes (for example, taxpayers in a specific retail sector that have a specific sales-to-reported-tax ratio).
Data mining technologies do the same thing, but on a much bigger scale. Using data mining
techniques, tax agencies can analyze data from hundreds of thousands of taxpayers to identify common attributes and then create profiles that represent different types of activity. Agencies, for example, can create profiles of high-yield returns, so auditors can concentrate resources on new returns with similar attributes. Data mining enables organizations to leverage their data to understand, analyze, and predict non-compliant behavior.
Data mining is the exploration and analysis, by automatic or semiautomatic means, of large quantities of data in order to discover meaningful patterns and rules. Organizations use this information to detect existing fraud and noncompliance, and to prevent future occurrences.
This recently announced restructuring of the IRS' LB&I is aiming to bring about a “cultural change” in the department, said Sergio Arellano, director of International Business Compliance for LB&I.
Read more at: Tax Times blog
