Tax Gap: IRS Can Improve Efforts to Address Tax Evasion by Networks of Businesses and Related Entities
Published: 2010-09-24T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: A taxpayer can control a group of related entities--such as trusts, corporations, or partnerships--in a network. These networks can serve a variety of legitimate business purposes, but they also can be used in complex tax evasion schemes that are difficult for the Internal Revenue Service (IRS) to identify. GAO was asked to (1) describe what IRS knows about network tax evasion and how well IRS's traditional enforcement programs address it and (2) assess IRS's progress in addressing network tax evasion and opportunities, if any, for making further progress. To do this, GAO reviewed relevant documentation about IRS programs and interviewed appropriate officials about those programs and IRS's plans for addressing such tax evasion. GAO also interviewed relevant experts and agency officials in developing criteria needed to perform the assessment.
section_title: What GAO Found
paragraphs: IRS views network-based tax evasion as a problem but does not have estimates of the associated revenue loss in part because data do not exist on the population of networks. IRS does know that at least 1 million networks existed involving partnerships and similar entities in tax year 2008. IRS also knows that many questionable tax shelters and abusive transactions rely on the links among commonly owned entities in a network. IRS generally addresses network-related tax evasion through its examination programs. These programs traditionally involve identifying a single return from a single tax year and routing the return to the IRS division that specializes in auditing that type of return. From a single return, examiners may branch out to review other entities if information on the original return appears suspicious. However, this traditional approach does not align well with how network tax evasion schemes work. Such schemes can cross multiple IRS divisions or require time and expertise that IRS may not have allocated at the start of an examination. A case of network tax evasion also may not be evident without looking at multiple tax years. IRS is developing programs and tools that more directly address network tax evasion. One, called Global High Wealth Industry, selects certain high-income individuals and examines their network of entities as a whole to look for tax evasion. Another, yK-1, is a computerized visualization tool that shows the links between entities in a network. These efforts show promise when compared to GAO's criteria for assessing network analyses. They represent new analytical approaches, have upper-management support, and cut across divisions and database boundaries. However, there are opportunities for more progress. For example, IRS has no agencywide strategy or goals for coordinating its network efforts. It has not conducted assessments of its network tools, nor has it determined the value of incorporating more data into its network programs and tools or scheduled such additions. Without a strategy and assessments, IRS risks duplicating efforts and managers will not have information about the effectiveness of the new programs and tools that could inform resource allocation decisions. Among other items, GAO recommends that IRS establish an IRS-wide strategy that coordinates its network tax evasion efforts. Also, IRS should assess its network programs and tools and should evaluate adding more data to its current tools. IRS generally agreed with these recommendations and noted additional organizational changes the agency is making that will address networks.

section_title: Letter
paragraphs: Taxpayers can use networks to carry out a wide variety of legitimate  business functions; however, networks also can be used to evade federal  tax obligations. A network is a collection of entities linked through direct  ownership or through common owners, associates, or shareholders. For  example, a network may consist of a taxpayer who owns a corporation  that does business with a partnership in which the same taxpayer is a  majority shareholder.
Network-related federal tax evasion occurs when the network’s taxpayers  improperly structure complex transactions among commonly held entities.  This allows the taxpayers to shift expenses or hide income, resulting in  lost federal revenue. In one recent example, the U.S. Court of Appeals for  the Ninth Circuit held that a family used transactions between commonly  owned entities to improperly eliminate $200 million in capital gains and  avoid $4 million in taxes. The extent of network-related tax  noncompliance has not been estimated, but in a 2007 tax compliance  forum we sponsored with the Joint Committee on Taxation and the  Congressional Budget Office, tax policy experts cited flows of income  among related entities as a potentially large problem.  Some of the  participants also said the Internal Revenue Service (IRS) needed to be  more innovative in auditing such flows.
IRS recognizes the risk posed by network-related tax evasion and is  developing new tools and programs to better identify and pursue such  evasion. For example, one new tool helps examiners graph the  relationship among entities in a taxpayer’s network using information  collected in an IRS database. IRS’s new tools and programs are in various  stages of development—some have yet to be fully implemented—and their  potential effectiveness is not known.
Given the above, you asked for more information about network-related  tax evasion and IRS’s efforts to combat it. Specifically, you asked us to   (1) describe what IRS knows about network tax evasion and how well  IRS’s traditional enforcement programs address network tax evasion and  (2) assess IRS’s progress in addressing network tax evasion and  opportunities, if any, for making further progress.
To describe what IRS knows and how well its traditional enforcement  efforts address network-related tax evasion, we reviewed IRS planning  documents and statistics, and interviewed relevant officials at IRS. We also  developed a video with technical assistance from IRS’s tax enforcement  experts that illustrates a hypothetical example of a network tax evasion  scheme. To assess IRS’s progress and opportunities for further progress,  we compiled an inventory of IRS’s network compliance efforts by  reviewing IRS documentation on auditing procedures for network-related  cases and interviewing officials involved with identifying and addressing  tax evasion related to networks. Because we could not find existing  criteria that could be used for assessing IRS’s network compliance efforts,  we developed criteria. To do this, we conducted semistructured interviews  with researchers who specialized in areas such as network analysis and  computer science, interviewed relevant officials at select federal agencies  that operate programs analyzing related entities or networks, and  interviewed IRS examiners about their work. We selected the researchers  based on our literature search of studies applying network analysis  techniques to noncompliance behavior as well as from referrals from the  researchers we had already interviewed. We distilled the common themes  our interviews to establish criteria. Officials responsible for IRS’s  compliance programs concurred with the criteria.  Using our work on the  inventory of IRS’s efforts, we then compared the status of each of the  efforts against the criteria.
We determined for the purposes of this review that the data used were  reliable. (See app. I for details on our scope and methodology.) We  conducted this performance audit from June 2009 through September 2010  in accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives.
subsections: section_title: Background
paragraphs: Networks of related entities are a feature of modern business  organizations. Many legitimate reasons explain why a business owner (or  owners) may choose to use a network of related entities to conduct  operations. While this list is not exhaustive, a network may be used  legitimately to:  isolate one line of business from the potential liabilities or risk of business  loss of another;  isolate regulated industries into separate entities to manage ownership,  reporting, or licensing requirements;    manage a business’s financing arrangements;    separate ventures based in different states and countries; or    separate activities for which ownership is restricted from those for which  ownership is not restricted (such as when subchapter S corporation  ownership restrictions apply).
A variety of entities can be linked in networks and report taxes in different  ways. Table 1 briefly describes some of the entities that will be discussed  in this report.  Certain entities file tax returns with IRS to report their  taxes owed, such as subchapter C corporations (C corporations), which  file Form 1120, U.S. Corporation Income Tax Return. Other entities may  operate as pass-throughs. A pass-through entity generally has the legal  right to impute or pass net income or losses through to its partners,  shareholders, and beneficiaries untaxed. Pass-through entities are required  to provide each partner, shareholder, or beneficiary with a Schedule K-1  stating the individual share of net income or loss to be reported. The  entities also provide this information to IRS. The partners, shareholders,  or beneficiaries are responsible for reporting this income or loss on their  individual income tax returns and paying any tax. Entities that may serve  as pass-throughs include subchapter S corporations (S corporations) and  partnerships.
The various types of entities that make up networks can be linked in  multiple ways. Table 2 summarizes how a select set of individuals, various  forms of businesses, and trusts may own or control other entities.  The  ownership, beneficiary status, or family connections within a network may  not be initially apparent. Individual entities connected to an owner may in  turn own other entities or be the beneficiaries of other trusts, thus creating  the potential for a large, complex network. For illustrative purposes,  appendix II includes diagrams showing how certain entities can be linked  and how the income generated by these entities can pass around a  network.
When taxpayers use multiple pass-through entities, they create what IRS  calls a “multitiered” network. In IRS’s definition, a multitiered network  exists when a pass-through entity is itself a partner, shareholder, or  beneficiary of another pass-through entity, leading to a situation where  income is allocated from one pass-through entity to another.  Figure 1,  adapted from an IRS study, is an illustration of a hypothetical, complex  network. In IRS’s example, the allocation from the observed partnership  on the far left side of the diagram crosses nine pass-through entities along  the red line before it reaches one of its ultimate owners on the right.
Owners of a network could use transactions among entities in the network  to create tax evasion schemes in which taxpayers   improperly conceal property ownership or income by diverting assets or  income from one entity to another;   improperly inflate an asset’s basis to reduce capital gains taxes by selling  the asset within the network;  improperly generate losses or tax deductions, which are passed through to  other entities that use the losses or tax deductions to offset gains;   inappropriately shift losses, deductions, or credits from entities not  subject to U.S. federal income tax, such as foreign entities, to those who  are; or   inappropriately shift income from those entities subject to U.S. federal  income tax to those entities that are not.
One example of a network tax evasion scheme is what IRS calls an  installment sale bogus optional basis transaction (iBOB). In an iBOB  scheme, taxpayers use commonly owned or controlled entities to  artificially adjust the basis of an asset and evade capital gains taxes. The  scheme can involve many layers of ownership, can take place over many  tax years, and can be shrouded by legitimate transactions. IRS  understands how the scheme works and has alerted examiners to its  existence. IRS’s Web site also describes a similar abusive transaction. In  cooperation with IRS, we developed a video explaining how an iBOB  works and the challenges IRS faces in ensuring those who are engaged in  such schemes are caught. In our simplified example, a hypothetical  taxpayer, Mr. Jones, sells a hotel that has appreciated in value resulting in  capital gains that are taxable income. Mr. Jones uses an iBOB to evade  paying capital gains taxes.
Successful tax evasion schemes exacerbate the tax gap, which is the  difference between the tax amount—including individual income,  corporate income, employment, estate, and excise taxes—that should  have been paid voluntarily and on time and the amount that was actually  paid for a specific year. IRS most recently estimated that the tax gap was a  net of $290 billion in 2001.  The tax-gap estimate relies on National  Research Program (NRP) data. NRP compiled data on taxpayers’  noncompliance by randomly sampling from the population of individual  filers, intensively reviewing tax returns in the sample to determine the  extent of noncompliance, and using the sample results to produce  noncompliance estimates for the entire population.
IRS has four operating divisions—Wage and Investment (W&I), Small  Business/Self-Employed (SB/SE), Large and Mid-Size Business (LMSB),  and Tax Exempt and Government Entities (TE/GE). Each division has its  own compliance programs, such as conducting examinations. W&I  generally addresses individual taxpayers filing Form 1040; SB/SE  addresses small businesses with assets of less than $10 million and self- employed taxpayers; LMSB addresses C corporations, S corporations, and  partnerships with assets of $10 million or more; and TE/GE addresses  pension plans, exempt organizations, and government entities. IRS’s  Criminal Investigation (CI) unit also investigates cases of fraud that may  involve networks. CI has investigative jurisdiction over tax, money  laundering, and bank secrecy laws.
The Office of Tax Shelter Analysis (OTSA), the Lead Development Center  (LDC), and the Servicewide Abusive Transaction Executive Steering  Committee (SAT ESC) are three groups within IRS with responsibilities  that may touch on network tax evasion. OTSA is an LMSB group that  collects and analyzes information, some of which is reported by  taxpayers about certain tax shelters.  LDC in SB/SE acts as the  clearinghouse to receive, identify, and develop leads on individuals and  entities that promote and/or aid in the promotion of abusive tax avoidance  transaction schemes. IRS has charged SAT ESC with coordinating  information about tax shelter schemes—including those that might involve  networks—that individual operating divisions identify.
section_title: IRS Suspects Networks Pose a Growing Tax Evasion Risk but Faces Barriers in Addressing the Risk through Its Traditional Enforcement Efforts
subsections: section_title: IRS Does Not Know the Magnitude of Network Tax Evasion, but Has Observed an Increase in Risk Factors for Such Evasion
paragraphs: IRS does not have an estimate of the total amount of revenue lost through  network tax evasion because of cost and complexity constraints. IRS faces  challenges in developing an NRP-type study to estimate the amount of  network tax evasion because it does not know the population of networks.  Therefore, IRS does not know what portion of the $290 billion net tax gap  is network related. Nor does IRS routinely track the amount of network  tax evasion it identifies through its enforcement programs. As will be  discussed in more detail below, IRS’s enforcement programs have  traditionally focused on single entities as the unit of analysis, such as an  individual or corporation, rather than networks.
While IRS does not know the population of networks, it has estimated the  size of a subset of that population. Based on a study of networks with two  or more pass-through entities, IRS estimated that in tax year 2008, more  than 1 million of these networks existed, of which about 2 percent had 11  or more different entities’ returns.
Although IRS lacks an estimate of network tax evasion, IRS officials said  they have evidence of a problem because of their experiences with abusive  tax shelters. Some of the tax schemes that IRS considers impermissible  necessarily involve, or could involve, networks. IRS maintains a list of tax  avoidance transactions on its Web site; any taxpayer engaging in such a  listed transaction, or a transaction substantially similar to a listed  transaction, must disclose to IRS certain information about that  transaction. IRS’s list of tax avoidance transactions includes examples of  abusive tax shelters involving networks.  The iBOB scheme previously  described is an example of network tax evasion involving a tax shelter.
IRS officials have cited trends that they said indicate an increased risk of  network tax evasion. These officials noted the increased use of pass- through entities. This suggested to them that the use of networks is  growing, that networks are becoming increasingly complex, and that the  risk of tax evasion is growing. Figure 2 illustrates the extent to which  partnership and S corporation tax return filings have increased from  creased from  calendar years 1998 to 2008.   calendar years 1998 to 2008.
Schedule K-1 filings from pass-through entities also have increased. From  2008 to 2009, submission of Schedules K-1 increased from 19.8 million to  21.2 million for partnerships filing Form 1065, U.S. Return of Partnership  Income. Meanwhile, submission of Schedule K-1 forms for S corporations  filing Form 1120S, U.S. Income Tax Return for an S Corporation, stayed  about the same at about 7 million from 2008 to 2009.
IRS examiners we spoke with who have experience in network-related  examinations said that, anecdotally, they have noticed an increase in the  use of disregarded entities in a network, which they said is another risk  factor for network tax evasion. A disregarded entity can be part of a  network, but its connection to a taxpayer may not be clear in the tax  information IRS uses to detect network tax evasion. The total number of  disregarded entities is unknown, but IRS estimated that there were at least  443,000 disregarded entities during a period between July 2007 and August  2008.
section_title: IRS’s Traditional Enforcement Programs Are Not Designed to Detect Network Tax Evasion
paragraphs: IRS’s programs for addressing network-related tax evasion include its  examinations (or audits) in which IRS examiners analyze taxpayers’  records to ensure that the proper tax was reported. IRS’s examination  practices have made contributions to tax enforcement. In fiscal year 2009,  IRS examined 1.6 million tax returns, identifying over $49 billion in  additional recommended tax.
IRS traditionally has conducted examinations on a return-by-return basis,  beginning with a single tax return in a particular tax year as the unit of  analysis and examining other tax returns connected with the original  return, if necessary, in what can be called a bottom-up approach.  The  examination selection process generally involves identifying a pool of  high-risk returns and from that group, determining which returns to  examine.
CI follows a similar approach. It starts with a taxpayer suspected of  criminal violations of the Internal Revenue Code or related financial  crimes and then branches out to related entities. When investigators want  to find connections between the suspected taxpayer and other entities,  they use Reveal, a network visualization tool. Reveal draws on data from  multiple sources that CI uses to analyze intelligence and to detect patterns  of criminal and terrorist activities. Data that Reveal uses include certain  cash transactions, tax information, and counterterrorism information. Its  outputs include a visual representation containing names, Social Security  numbers, addresses, and other personal information of individuals  suspected of financial crime or terrorist activity. Because of CI’s authority  to access sensitive information, only in rare instances do non-CI staff use  Reveal, according to CI data security staff.
IRS’s traditional enforcement efforts are not designed to identify  networks, select those networks that appear to be involved in tax evasion,  or follow up with in-depth examination or investigation. Specifically, IRS’s  traditional efforts are challenged in dealing with network tax evasion by  the combined effects of a number of factors such as the following.
A bottom up approach focusing on a single taxpayer. As with the  businesses in the iBOB scheme previously described, an entity involved in  a network may not raise suspicions when examined in isolation.  The tax  evasion may only be apparent in how it relates to other entities in the  network.   Internal divisional boundaries. A single network may contain many types  of entities that cross the responsibilities of IRS’s operating divisions (i.e.,  W&I, SB/SE, LMSB, TE/GE). While IRS has the SAT ESC in place for  overseeing abusive transaction issues, examiners on any particular audit  may not have the expertise or authority to pursue the network  connections of the taxpayer under review. For example, SB/SE examiners  auditing a small partnership may not have the time, expertise, or authority  to recognize or pursue a related large S corporation that is a member of  the partnership.
Single tax year examinations. IRS examiners typically begin  examinations by looking at tax return data for a single tax year, limiting  their opportunity to notice multiyear schemes. The iBOB is an example of  a scheme in which the transactions creating the tax evasion can occur in  multiple tax years.
Competing time and resource priorities. IRS generally aims to conduct  examinations in a manner that maximizes the amount of tax  noncompliance found while minimizing an examiner’s time commitment.  Network examinations may be highly time-consuming for an examiner and  the outcome is less predictable.
Examiners’ ability to follow network connections also is restricted in  another way. The Taxpayer Browsing Protection Act prohibits federal  employees from willfully inspecting taxpayer information without  authorization. To comply with the law, IRS restricts the access examiners  have to certain tax information. According to IRS, the law helps protect  the confidentiality of taxpayer information, but examiners told us it also  may restrict an examiner’s flexibility to explore leads, without manager  approval, across different tax forms that could reveal network abuse.
section_title: IRS’s Recent Efforts to Better Detect and Pursue Network Tax Evasion Show Promise, but Opportunities Exist for Additional Progress
subsections: section_title: IRS Is Developing Programs and Tools Intended to Help Address Network Tax Evasion
paragraphs: IRS has been creating specific programs and tools that address network  tax evasion more directly than its traditional examination approach.
subsections: section_title: Global High Wealth Industry (GWHI)
paragraphs: Under GHWI, IRS identifies certain high-wealth individuals and then  examines each individual’s network.  According to the Commissioner of  Internal Revenue, the intent is to take a unified look at the entire complex  web of business entities controlled by a high-wealth individual to assess  the tax compliance of the network, rather than of the separate entities  individually. IRS initiated GHWI in 2009. Although it resides in LMSB, IRS  plans for GHWI to include staff with expertise that crosses divisional  boundaries. For example, GHWI examiners might address small  partnerships included in a network, even though small partnerships  otherwise would be under the purview of SB/SE. As a result, GHWI is  expected to directly examine tax issues that otherwise might have been  missed.
section_title: Enhancing Ownership Transparency (EOT)
paragraphs: SB/SE launched the EOT project in January 2010 to gather data on the  owners and locations of new businesses through employer identification  number (EIN) applications. Primarily, IRS officials said they are  interested in identifying what they refer to as the responsible party, which  is the true beneficial owner of the business in this context. As of January  2010, the EIN application form requests additional information from  business owners, such as the Social Security number of the responsible  party and location of incorporation, which IRS previously did not request.  The goal of the project is to link the new data to existing information in  IRS’s databases for identifying related businesses or a network of  businesses. As the operating division responsible for the EIN process, W&I  will implement the program once design is complete, which is tentatively  scheduled for January 2012.
The network tool that is furthest along in development and most widely  used at IRS is yK-1. yK-1 is a network visualization tool that is now being  used by some IRS staff in doing examinations and in reviewing networks.  Users enter a taxpayer identification number (TIN) into the yK-1  software, which produces a picture showing how that TIN is connected to  other entities through information filed on Schedule K-1. Figure 3 shows  an example of yK-1 output. In this example, the numbers along the arrows  represent the flow of money among the three different types of entities.  yK-1 diagrams can help examiners and other yK-1 users determine a  specific entity’s sources and amounts of income and whether other entities  in the taxpayer’s network need further examination.  Examiners and users  can then access other tax information about the entities in the network  from IRS databases as well as non-IRS information from sources such as  Accurint, a financial information database, and the Internet. Programmers  are continuing to work on expanding yK-1’s capabilities, such as adding  estate and gift tax data and data on international taxpayers.
IRS’s Research, Analysis and Statistics group is developing another tool  related to yK-1 called GraphQuery. GraphQuery is a pattern-matching tool  being designed to facilitate top-down identification and selection of those  networks that have the highest risk for noncompliance.  In this top-down  approach, users would enter into GraphQuery a specified pattern, such as  the structure of a known tax evasion scheme; the program would search  for other networks showing the same pattern and list the TINs of the  entities in those networks.
IRS’s Office of Performance Evaluation and Risk Analysis has  demonstrated a program called NetReveal, which can build a diagram of  related entities or individuals using  a wider variety of data, including  nontax return data, than are used by yK-1. NetReveal, which is unrelated  to CI’s Reveal program, remains under consideration by IRS and has not  yet been made available to the operating divisions for tax compliance  purposes.
section_title: IRS’s Ongoing Development of New Programs and Tools Is Generally Consistent with Network Analysis Criteria but Further Progress May Be Hindered by the Lack of a More Strategic Approach
paragraphs: Judged according to 14 network analysis criteria we developed, IRS’s work  on creating new network programs and tools already shows promise. We  used interviews with academic experts and users of network analysis  programs at other federal agencies to develop the 14 criteria, which are  listed in table 3, for assessing network analysis programs and tools.   Appendix I discusses what the criteria entail and how we developed them.
While the criteria describe good management practices that could apply to  a wide variety of programs, the experts we spoke with cited these criteria  as directly relevant to network analysis. In particular, the criteria highlight  the crosscutting nature of network-related problems.  The experts we  spoke with also noted that network problems, such as network tax  evasion, include by definition multiple entities that could cut across  databases and an oversight agency’s organizational units.  As a logical  consequence, they emphasized using criteria that call for an agencywide  strategy, access to a wide range of data, and good collaboration across an  agency’s organizational structures.
For IRS, criteria focused on the crosscutting nature of network analysis  are directly relevant to the problem of network tax evasion. A variety of  entities could comprise a network, which could be under the purview of  different IRS divisions.  Similarly, data on the tax accounts for the  different entities could be in different IRS databases.
Our assessment of IRS’s new programs and tools against the criteria is  shown in table 3.  The assessment is of IRS’s progress to date—the  programs and tools are still under development.  The assessment also  indicates areas where IRS’s efforts to date do not satisfy the criteria.   These areas present opportunities to make further progress.
As table 3 indicates, IRS’s efforts to focus more directly on network tax  evasion, while still under development, are consistent with our criteria for  judging network analyses but do not fully satisfy them.  The efforts are  supported by upper management, offer new analytical approaches that  more directly address network tax evasion, and attempt to cut across IRS’s  divisional boundaries and databases. As a result, these efforts show  promise at being able to detect and pursue network tax evasion more  effectively than IRS’s traditional enforcement programs.
However, table 3 also shows where opportunities exist to provide more  overall direction to IRS’s efforts and perhaps hasten the development of  specific programs and tools. For example, the table notes the lack of  agencywide strategy and goals for IRS’s various network efforts that are  spread throughout the agency. Without agencywide strategy or goals to  coordinate and prioritize these efforts, two risks exist. First, IRS could  make redundant investments; second, IRS could fail to concentrate  investments on the programs and tools with the greatest potential.
IRS may need to take an incremental approach to managing these risks  because of the uncertainties. As already discussed, the population of  networks is not known, networks can be complex, and IRS does not know  which programs and tools will be most effective. Further, the costs could  be significant. IRS’s current organizational structure, work processes, and  data systems do not support using a network as a unit of analysis and  adjusting them to do so could disrupt other important priorities and  programs. In light of this uncertainty about potential benefits and the cost,  IRS will need to be careful in reallocating resources from other  compliance programs to its new network efforts. As IRS gathers more  information, management will be better positioned to more fully develop  its strategy.
IRS also faces challenges in responding to the criteria for programming  and data. As noted in table 3, adding new data, updating existing data, and  making existing data more readily available in electronic form all could  enhance IRS’s capabilities to identify and pursue network tax evasion.   Similarly, IRS could potentially benefit from more complete consideration  of the potential relevance of the array of analytical techniques developed  in the research literature and available in existing software applications.  However, as also noted above, such efforts would have costs.  This  reinforces the need for a strategy that would prioritize investments in  better data and analytical capabilities.
IRS has not assessed the impact and effectiveness of its new network  analysis tools, as described in table 3.  For example, the benefits of using  yK-1 relative to the additional time it takes examiners to use it have not  been studied, but anecdotal evidence from users and management indicate  yK-1 is a useful tool. Effectiveness assessments have costs which can be  managed by judgments about the depth of the assessment needed.  However, without effectiveness assessments, IRS managers are left  without information that could help with the development of a strategy  and with decisions about prioritizing investments in better data.
Table 3 stresses the importance of regular communications and training  among all types of staff involved in identifying, analyzing, and pursuing  network evasion schemes.  Without these, auditors could be missing  information they need and network schemes could go undetected. IRS  officials said that the direct costs for these actions tend to be minor, but  that they must be mindful of how these actions might affect other  priorities. For example, they said the initial 2-hour training for yK-1  imposes minor costs. However, the officials also said that learning yK-1  requires accessing it enough to appreciate all of its capabilities.
section_title: Conclusions
paragraphs: IRS’s network compliance efforts have the potential to address a  significant part of the tax gap.  However, IRS does not know the extent of  this compliance problem or how effective its new programs and tools will  be.  Nor does IRS have a strategic approach to coordinate these network  efforts across the agency.
IRS needs to walk a middle course between doing too much too soon  versus doing too little too slowly.  If it does too much, IRS risks taking  resources from other priorities without assurance that the investment in  the network efforts will reduce network tax evasion.   If it does too little,  IRS runs the risk of not learning more about networks and how to detect  their tax evasion schemes.
To successfully balance these trade-offs, IRS would benefit from having a  more strategic approach to coordinate and focus its various network  efforts across the agency. As IRS learns more, that strategic approach  would work toward developing a set of goals and measures to guide future  efforts and consider ways to assess the impacts of the various programs  and tools that are to be developed. Effectiveness assessments have costs,  but without any assessments, managers lack valuable information. With  such information, IRS would have a better sense of the pace at which it  should invest its resources into expanding the network analysis program,  including adding the analytical tools, data, and staff expertise that would  be needed to address the specific compliance issues that IRS would be  discovering. IRS’s efforts to develop network programs and tools would  also be enhanced by ensuring that staff understand the benefits of using  the tools and are provided with a mechanism to provide feedback on the  tools’ and programs’ effectiveness.
section_title: Recommendations for Executive Action
paragraphs: We recommend that the Commissioner of Internal Revenue take the  following three actions.
Establish an IRS-wide strategy with goals, which may need to be  developed incrementally, to coordinate and plan ongoing and future  efforts to identify and pursue network tax evasion. The strategy should  include:    assessing the effectiveness of network analysis tools, such as yK-1;    determining the feasibility and benefits of increasing access to existing  IRS data, such as scanning additional data from Schedule K-1, or  collecting additional data for use in its network analysis efforts;    putting the development of analytical techniques and tools that focus  on networks as the unit of analysis, such as GraphQuery, on a specific  time schedule; and    deciding how network efforts will be managed across IRS, such as  whether a core program team or management group is needed.
Ensure that staff members who will be using current and additional  network tools fully understand the tools’ capabilities.
Establish formal mechanisms for front-line users to interact directly with  tool programmers and program analysts to ensure future network analysis  tools, such as GraphQuery, are easy to use and help achieve goals.
section_title: Agency Comments and our Evaluation
paragraphs: In a September 8, 2010, letter responding to a draft of this report (app. IV),  IRS’s Deputy Commissioner for Services and Enforcement provided  comments on our findings and  recommendations as well as information  on additional agency efforts, changes, and studies to address network tax  noncompliance. The letter also provided technical comments that we  incorporated into our report as appropriate.
The Deputy Commissioner said that IRS agreed with our draft on the  challenges involving network tax compliance and the status of IRS’s  network-related efforts. IRS also generally agreed with our  recommendations to establish an IRS-wide strategy with goals, ensure that  staff understand the capabilities of IRS’s network tools, and establish a  more formal way for IRS staff to collaborate as new network tools are  developed and implemented.
In agreeing with our strategy recommendation, IRS’s response noted that  it may be more effective for IRS to consciously and appropriately include  network issues in broader strategic plans, rather than develop a separate  strategy for networks. We agree. Our recommendation is that IRS develops  a strategy. We leave IRS with the discretion on how to articulate the  strategy and point out that it may need to be developed incrementally.
As agreed with your offices, unless you publicly release the contents  earlier, we plan no further distribution of this report until 30 days from its  date. At that time, we will send copies to interested congressional  committees, the Secretary of the Treasury, the Commissioner of Internal  Revenue, and other interested parties. The report will also be available at  no charge on the GAO Web site at http://www.gao.gov.
If you or your staff have any questions about this report, please contact me  at (202) 512-9110 or whitej@gao.gov. Contact points for our Offices of  Congressional Relations and Public Affairs may be found on the last page  of this report. GAO staff who made major contributions to this report are  listed in appendix V.
section_title: Appendix I: Objectives, Scope, and Methodology
paragraphs: The objectives of this report were to (1) describe what the Internal  Revenue Service (IRS) knows about network tax evasion and how well  IRS’s traditional enforcement efforts address network tax evasion and (2)  assess IRS’s progress in addressing network tax evasion and opportunities,  if any, in making further progress.
To describe what IRS knows about network tax evasion and how well the  traditional enforcement efforts the agency has in place address network  tax evasion, we reviewed IRS statistics, policy manuals, and planning  documents, including strategic plans. We also interviewed relevant IRS  officials and staff.
We developed a video to highlight one type of network tax evasion. To  develop this video describing the installment sale bogus optional basis  adjustment (iBOB), we reviewed IRS technical information on iBOB  schemes, out of which we developed a simplified, hypothetical example.  IRS suggested the iBOB as a scheme that would make an appropriate  example. IRS management and technical staff reviewed the video  throughout its development, and we incorporated their technical  comments where appropriate.
Because no existing criteria that we could find directly applied to  reviewing the progress of IRS’s network analysis programs, we developed  our own. We conducted two groups of interviews with network analysis  users and experts to develop our criteria for network analysis program  development and implementation. The first group of interviews was with  academic researchers considered to be experts whom we identified  through detailed literature reviews and recommendations from other  experts. The second group of interviews was with federal agencies that  use network analysis tools. We also interviewed IRS auditors about their  work.
To identify relevant academic experts, we reviewed the research literature  using network analysis and related methods. We then created a literature  search matrix and entered all studies obtained through the search that  involved some quantitative/automated form of network analysis and an  empirical application to a substantive area that had potentially direct  applications to our review. We selected a subset of these studies for a  more detailed review and used professional judgment to focus on studies  of most immediate relevance. The literature review was the primary tool  used for selecting researchers and experts for further follow up, which  was ultimately based on ensuring a balance of experts with expertise  across the entire array of substantive research topics and methodological  approaches that we identified in our search and on determining that  individual experts’ research agendas were both broad and deep. We  conducted semistructured interviews with these experts, during which  time we asked for recommendations of other network analysis and data  mining experts. Often these recommendations were for researchers or  experts we had already identified. Not every expert we identified was  available to speak with us. The experts we spoke with included Wayne E.  Baker, University of Michigan; Stephen P. Borgatti, University of  Kentucky; Kathleen M. Carley, Carnegie Mellon University; Sean Everton,  Naval Postgraduate School; Mark Granovetter, Stanford University; David  Jensen, University of Massachusetts Amherst; Mark Mizruchi, University  of Michigan; Carlo Morselli, University of Montreal; Daniel M. Schwartz,  Criminal Intelligence Service Ontario; Duncan Watts, Yahoo! Research;  and Jennifer Xu, Bentley University. We used our interviews with these  experts to aid only in developing our criteria; they did not otherwise  contribute to the content of the report.
To identify federal agencies to interview, we first reviewed academic  literature and reports on government agencies that conduct network  analyses, including our own reports. Through this review, we identified  Customs and Border Patrol (CBP); Federal Bureau of Investigation;  Financial Crimes Enforcement Network; Immigration and Customs  Enforcement; Risk Management Agency at the United States Department  of Agriculture; and the Securities and Exchange Commission. We also  identified the Financial Industry Regulatory Authority (FINRA), which is  not a federal agency but has an oversight and enforcement component  similar to that of federal financial regulators. We conducted  semistructured interviews with relevant program staff that use network  analysis tools at these agencies and FINRA. During each interview, we  asked about what works well in their network analysis program; what  about their network analysis program needs improvement; what tools they  feel could improve their program; what are best practices for developing a  network analysis program; and what other agencies use network analysis  programs. Of those other agencies that were mentioned that had network  analysis programs, we chose not to meet with the Central Intelligence  Agency and National Security Agency due to time constraints and data  sensitivity issues. The Drug Enforcement Agency (DEA) was also  recommended to us to speak with; while we did not directly speak with  DEA due to time constraints, we were able to speak with a DEA liaison at  CBP who briefly described DEA’s network analysis program.
From these two rounds of interviews, we distilled the common themes in  those responses to establish the criteria. We first read through all the  interviews, recording potential criteria. We then systematically reviewed  the entire set of interviews to identify all that contained our initial criteria;  this resulted in the rephrasing or elimination of some of these criteria, as  well as the addition of a number of new ones. The themes that emerged  from the interviews fell into the following categories: strategy,  management support, program evaluation, data management, staffing,  collaboration, methodology, and other. We determined that for our review  of IRS, it would not be appropriate to set criteria for the exact  methodology the agency should use in its network analysis program, or  particular software packages. Therefore, we eliminated any particular  research or methodological approaches and techniques from our final  criteria list. We also eliminated ideas where there was a clear division of  opinion among the experts we interviewed. For example, experts and  users did not agree on the benefits of including narrative data in network  analysis programs. We presented the criteria to IRS for its feedback.
The final 14 criteria were categorized by theme: overall strategy;  programming and data; and collaboration. The criteria were neither  prioritized nor made to be specific to IRS. Each criterion was supported,  at minimum, by five interviews; many were supported by eight or more  interviews. The criteria with the fewest interviews for support generally  pertain to organizational structure issues. The academic experts generally  did not address these issues because they tend to use network analysis  programs for research purposes compared to a federal agency’s use for  enforcement purposes. In these instances, we also had support from prior  GAO work.
To assess IRS’s progress in identifying and addressing network tax  noncompliance, we reviewed IRS documentation on its auditing  procedures and interviewed officials involved with identifying and  addressing noncompliance related to networks. We then compared the  evidence we collected in these reviews and interviews with the criteria we  had developed and identified specific instances where IRS has  demonstrated progress towards meeting each of the criteria. We also  identified opportunities for further progress in meeting the criteria. We  determined for the purposes of this review that the data used were  reliable. Because some of the efforts to address noncompliance were  under development during the time of our review, we presented the  assessment to IRS officials for their feedback and for related updates that  might affect the assessment.
Our review of key studies applying quantitative network analysis methods  to areas of noncompliance or illicit activity (see app. III) focused on  identifying analytical approaches that individuals developing network  analysis systems may find useful. The criteria for selection of these studies  were similar to those used in selecting experts. In particular we ensured  that the studies taken as a whole group covered a broad set of topics and  methodologies and also that the individual studies were supported by  broad and deep individual research agendas. We included two additional  criteria to ensure more direct applicability of the studies to the report  topic.
The selected studies applied network analysis directly to a specific set of  activities most directly related to the report topic, particularly criminal  intelligence, organized crime, fraud detection, public safety or security,  and international trafficking.
Studies focused on counterterrorism applications of network analysis and  studies in the link analysis research area, which is focused on algorithms  for identifying relationships among items in large databases of  textual/narrative information, were largely excluded.
We conducted this performance audit from June 2009 through September  2010 in accordance with generally accepted government auditing  standards. Those standards require that we plan and perform the audit to  obtain sufficient, appropriate evidence to provide a reasonable basis for  our findings and conclusions based on our audit objectives. We believe  that the evidence obtained provides a reasonable basis for our findings  and conclusions based on our audit objectives.
section_title: Appendix II: Example Diagrams of Network Relationships by Entity Type
paragraphs: A network may comprise a variety of entities. Four types of entities that  IRS recognizes that also can form networks are corporations,  partnerships, trusts, and individuals. While these are not the only types of  entities or connections that may exist in networks, they are entities that  the Internal Revenue Service (IRS) has emphasized. The following  examples show how networks can be connected and how income can flow  among different entities. These examples are hypothetical and any  resemblance with a known network is purely coincidental.
Figure 4 shows a network that includes two C corporations and two  partnerships. Here, income and tax attributes (such as expenses,  deductions, and losses) earned by the partnerships would flow back to C  corporation B. However, the extent that C corporation A might also have  received income from C corporation B depends on their business  arrangements. S corporation A and Individuals A and B represent other  partners that overlap with C corporation A’s network.
S corporations are pass-through entities that pass income on to  shareholders. In figure 5, Individual A receives income and other tax  attributes earned by S corporations A and B and Partnership A. Likewise,  Individuals B and C receive income and tax attributes earned by S  corporation C, which in turn receives income from Partnership B. S  corporation A passes income on to all three individuals in this example.
Figure 6 shows how partnerships can be layered and seemingly  unconnected individuals can be connected. Individuals A and D have no  direct connection but both ultimately receive income and other tax  attributes from Partnership A. Individuals A through D are also connected  to Individual E because of the financial ties between Individuals D and E  through Partnership X.
Trusts can be connected to other entities in a network in several ways. In  figure 7, Trusts A and B are partners in Partnership B and, along with  Individual A, receive income and tax attributes from Partnership B.  Trust  A is a type of trust that is allowed to own shares in S corporation X, which  passes income and tax attributes to its beneficiary, Individual B.  Trust C is  a partner in Partnership C and, along with Trust B, sends its income and  tax attributes to Individual D.
section_title: Appendix III: Overview of Studies Using Formal Network Analysis to Examine or Detect Criminal/Illicit Activity
paragraphs: This appendix presents examples of research that have used formal  quantitative network analysis techniques and methods to analyze network  noncompliance, criminal, or illicit activity. Individuals developing network  analysis programs may find relevant analytical approaches and techniques  from this research. The research is summarized under four approaches to  network analysis of illicit or criminal behavior. Key technical terms used  in this summary include the following.
Network: A set of actors and the set of ties representing some relationship  or lack of relationship between the actors.
Centrality: The number of direct contacts between a given network  member and all other network members.
Brokerage/network efficiency: Extent to which network members’  connections are not to each other or are not within the same group  (nonredundancy).
Cut-point: A network member that serves as the only connection among  people or groups of people within a network.
Key players: The set of network members whose removal will most disrupt  or who can most efficiently diffuse information through a network.
Density: The proportion of existing links out of all possible links in a  network.
Centralization: Extent to which a network is organized around a few  central members.
Clustering: Extent to which a network is subdivided into distinct, heavily  interconnected subgroups.
Connectedness: Extent to which all network members can reach each  other along unbroken paths.
Hierarchy: Extent to which the links in a network flow in one direction.    Chain: A network structure where a high proportion of network members  can only reach each other via some other network member.
subsections: section_title: I. Interpretive Approaches Using Visualization or Core Network Measures to Explain Criminal or Illicit Activity
paragraphs: These studies use network visualization and measurement techniques to  develop qualitative explanations of illicit/criminal behavior. This research  has two major implications for network tax noncompliance.
First, these studies suggest that both individual- and network-level  measures of position and structure may be useful diagnostics for  identifying criminal/illicit behavior. For example, higher centrality of  individuals in a network may be associated with higher levels of criminal  activity. Further, high levels of brokerage/network efficiency may be  associated with a leadership role in a criminal network and with success  in criminal activity. At the network level, chain structures often  characterize criminal networks, though more interconnected structures  (measured as density) in criminal networks may correspond with higher- risk activities.
Second, tracking how network measures change may provide insight into  how criminal behavior evolves. One study suggests that network leaders  more effectively disguise involvement in criminal networks over time by  using indirect relationships. Changes in the structure of noncompliance  networks may correspond to changes in strategy and management. For  example, management crisis in a noncompliance network may produce  new relationships between previously disconnected individuals or change  the extent of hierarchy in the network.
section_title: II. Approaches Using Visualization or Core Network Analysis Measures for Intervention and Enforcement in Criminal Networks
paragraphs: Numerous studies have used core network visualization and measurement  techniques to develop interventions into criminal networks for  enforcement purposes. A key implication is that intervention decisions  should arise from analyzing network structures and processes. These  studies suggest that effectively identifying intervention strategies may  require varied network analysis approaches, ranging from basic to  complex.
Basic network measures and constructs such as centrality or cut-points  may effectively identify interventions in some contexts but not others. For  example, in some contexts, using algorithms to find sets of key players  may enable more efficient disruption or surveillance of criminal networks  than approaches using centrality measures and cut-points. Extending the  key player approach by incorporating data on individuals’ or entities’  attributes also may help. Approaches identifying cohesive subgroups and  clusters, including the presence or absence of links between them, may  suggest effective interventions. For example, if a network has  disconnected or weakly connected subgroups that are themselves heavily  connected, it may be appropriate to focus on the more cohesive  subgroups, rather than on central individuals across the network.  Relatedly, it may be more productive to disrupt decentralized criminal  networks than more centralized ones.
section_title: III. Data-Mining and Related Approaches for Analyzing and Detecting Criminal/Illicit Activity
paragraphs: This research covers various approaches from the computational sciences,  relying on data-mining, machine learning, and simulation techniques. The  approaches develop methods that may help detect unknown aspects of  noncompliance networks. Possibilities include the following:  relational machine learning approaches that identify systems of statistical  relationships among attributes of network entities, such as individual  roles, status and experience, and firms’ locations, using complex relational  data;  risk-assessment methods that use probabilistic models to identify firms  and employees with a high risk for misconduct. For example, algorithms  have been developed to identify individuals that are atypically moving  together among locations or organizations, which may help assess non- compliance risk;    methods for identifying links, identities, or groups in a network. Link  prediction, for example, uses machine-learning to identify unknown links  in a network. An alternative method is anomalous link detection, which  identifies links that are more likely to involve illicit/criminal/fraud activity.  A third method is anonymous identity matching, which uses known  relationships of unknown entities to predict their identities. Pattern  matching or clustering algorithms can identify groups of entities  occupying similar positions in the overall network; and    dynamic simulation approaches that model networks’ likely responses to  varied interventions and assess the effectiveness of the interventions.
section_title: IV. Multivariate and Statistical Analyses of Associations and Causal Relationships among Network Variables and Key Behavioral Outcomes
paragraphs: These studies use regression analysis or other multivariate statistical  methods to examine collusion, unethical behavior, and adoption of illegal  innovations. They emphasize statistically significant associations and  causal relationships between measures of network position, overall  structure, behaviors, and outcomes, while controlling for an array of  individual and organizational attributes. Key implications include:    Comparing different types of networks by their structural characteristics  can help identify illicit activity. For example, illegitimate networks may be  more hierarchical than legitimate networks. A network’s need for secrecy,  typically associated with illicit activity, as well as its information- processing demands, may determine the degree of centralization exhibited  by the network.  Important relationships between network and non-network variables can  influence illicit behavior. For example, common codes of conduct may  mitigate the likelihood that hierarchical or asymmetric relationships  produce unethical behavior. While lower-status middle managers usually  are the most vulnerable network participants, higher-status upper-level  managers may be more vulnerable in centralized networks than in  decentralized networks.
section_title: Appendix IV: Comments from the Internal Revenue Service
paragraphs: Appendix IV: Comments from the Internal  Revenue Service  GAO recommends that the Commissioner of the Internal Revenue Service take the  following actions:  Establish an IRS-wide strategy with goals, which may need to be developed  incrementally, to coordinate and plan ongoing and future efforts to identify and pursue  network tax evasion.  The strategy should include:  1.  Assessing the effectiveness of network analysis tools, such as yK-1  2.  Determining the feasibility and benefits of increasing access to existing IRS data,  such as scanning additional data from Schedule K-1, or collecting additional data  for use in its network analysis efforts  3.  Putting the development of analytical techniques and tools that focus on  networks as the unit of analysis, such as GraphQuery, on a specific time  schedule  4.  Deciding how network efforts will be managed across IRS, such as whether a  core program team or management group is needed   Because of the broad impact of networks and the complex organizational responses  required, developing a separate strategy to address network compliance will be useful,  but may not be as effective as ensuring that network issues are consciously and  appropriately included in broader strategic plans.  In any event, a better articulated  strategy for deploying, maintaining, and improving tools for network analysis is needed.
The IRS agrees that it is useful to assess the effectiveness of analysis tools, but it would  be necessary to balance the costs of such an assessment.
The IRS agrees with this recommendation.
The IRS agrees that it would be useful to better structure and support the development  of analytical tools.
The IRS will look at this issue.  It may not be possible nor appropriate to manage  network compliance activity centrally.  However, at a minimum, we will consider how to  manage better the analytic tools and whether a core program team would be useful in  this regard.
Ensure that staff members who will be using current and additional network tools fully  understand the tools’ capabilities.
The IRS agrees that training of compliance employees in the use of analytic tools can  be improved.
Establish formal mechanisms for front-line users to interact directly with tool  programmers and program analysts to ensure future network analysis tools, such as  GraphQuery, are easy to use and help achieve goals.
Currently, the system developers have access to appropriate field employees, but we  will consider improving the ability of field employees to have direct input and feedback  to systems and risk assessment activities.
section_title: Appendix V: GAO Contact and Staff Acknowledgments
subsections: section_title: Acknowledgments
paragraphs: In addition to the contact named above, Tom Short, Assistant Director;  Lydia Araya; Katie Arredondo; Russ Burnett; David Dornisch; Robert  Gebhart; Eric Gorman; Sherrice L. Kerns; Melissa L. King; Adam Miles;  Danielle Novak; Melanie Papasian; Ernest L. Powell Jr.; and A.J. Stephens  made key contributions to this report.