When federal authorities want to ferret out abusive tax shelters, they send an army of forensic accountants, auditors and lawyers to burrow into suspicious tax returns.<\/p>\n
Analyzing mountains of filings and tracing money flows through far-flung subsidiaries is notoriously difficult; even if the Internal Revenue Service<\/a>manages to unravel a major scheme, it typically does so only years after its emergence, by which point a fresh dodge has often already replaced it.<\/p>\n But what if that needle-in-a-haystack quest could be done routinely, and quickly, by a computer? Could the federal tax laws \u2014 74,608 pages of legal gray areas and welters of credits, deductions and exemptions \u2014 be accurately rendered in an algorithm?<\/p>\n New academic research seeks to use artificial intelligence to combat tax evasion by corporate entities, from publicly traded multinationals to private partnerships. The goal is to give the I.R.S.<\/a> a better way to investigate sophisticated tax shelters that strip tens of billions of dollars from federal coffers each year.<\/p>\n <\/p>\n \u201cWe see the tax code as a calculator,\u201d said Jacob Rosen, a researcher at the Massachusetts Institute of Technology who focuses on the abstract representation of financial transactions and artificial intelligence techniques. \u201cThere are lots of extraordinarily smart people who take individual parts of the tax code and recombine them in complex transactions to construct something not intended by the law.\u201d<\/p>\n A recent paper<\/a> by Mr. Rosen and four other computer scientists \u2014 two others from M.I.T. and two at the Mitre Corporation, a nonprofit technology research and development organization \u2014 demonstrated how an algorithm could detect a certain type of known tax shelter used by partnerships.<\/p>\n First, the researchers translated tax regulations governing partnerships, a growing source of tax trickery, into source code. Then they rendered the transactions underpinning a questionable shelter known as \u201cinstallment-sale bogus optional basis,\u201d or Ibob, as a series of codes. The Ibob shelter artificially inflates the basis value of an asset on a tax return to wipe out taxable gains when that asset is sold. While some of Ibob\u2019s individual transactions are perfectly legal, the collective result is a bogus deduction.<\/p>\n Next, the researchers mapped out in code the tangle of entities that make up typical partnerships. The results flagged specific combinations of transactions and partnership structures that were likely to produce the Ibob dodge.<\/p>\n Large corporations attract most of the attention when it comes to tax avoidance and tax evasion, but partnerships, which have separate tax rules, are a growing source of worry for the authorities. Commonly used by hedge funds, private equity<\/a> funds, real estate outfits and oil and gas concerns, partnerships are far less likely to be audited than corporations. A Government Accountability Office r<\/a> eport from 2010<\/a> said that the I.R.S. knew of one million \u201cnetworks\u201d involving partnerships and similar entities, adding that \u201cthe I.R.S. also knows that many questionable tax shelters and abusive transactions rely on the links among commonly owned entities in a network.\u201d<\/p>\n Rooting out fraud in corporate tax returns takes place largely through data mining, in which the I.R.S. collects pre-existing data from filed tax returns and analyzes them for patterns. The data goes into a database within the agency\u2019s Office of Tax Shelter Analysis, created in 2000 in the wake of a crackdown on mass-market tax shelters sold by accounting firms, law firms and banks. The data-analytical approach depends upon already having some kind of smoking gun, such as a suspicious deduction on a return.<\/p>\n By contrast, the artificial intelligence approach does not require pre-existing evidence. Instead, it focuses on rule mining, in which individual tax code regulations are lined up against one another to ascertain if they can be used collectively to create a sophisticated tax dodge.<\/p>\n Rule mining takes advantage of a surprising feature of tax shelters: While their inner workings are convoluted and complex, their general aim at the highest level is usually simple and clear \u2014 to lower tax bills by improperly generating bogus losses, deductions, offsets and credits that minus the shelters would not exist.<\/p>\n \u201cIt\u2019s incredibly difficult to have a computer algorithm that duplicates the enormous creativity of taxpayers, but it\u2019s very promising,\u201d said Robert A. Green, a tax professor at Cornell Law School who read the M.I.T.\/Mitre paper.<\/p>\n An I.R.S. spokesman declined to comment.<\/p>\n Sanith Wijesinghe, one of the Mitre researchers, admitted that it was unclear whether other parts of the federal tax regulations could be turned into computer code as easily as partnership regulations.<\/p>\n \u201cWe\u2019re trying to automate as much of the parts that can be automated and still allow folks to continue the brainstorming,\u201d Mr. Wijesinghe said. \u201cBecause that\u2019s what the promoters are doing.\u201d<\/p>\n http:\/\/www.nytimes.com\/2015\/10\/10\/business\/computer-scientists-wield-artificial-intelligence-to-battle-tax-evasion.html?_r=0<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" When federal authorities want to ferret out abusive tax shelters, they send an army of forensic accountants, auditors and lawyers to burrow into suspicious tax returns. Analyzing mountains of filings and tracing money flows through far-flung subsidiaries is notoriously difficult; even if the Internal Revenue Servicemanages to unravel a major scheme, it typically does so … Continue reading Computer Scientists Wield Artificial Intelligence to Battle Tax Evasion<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[2],"tags":[],"_links":{"self":[{"href":"https:\/\/aireligion.org\/index.php?rest_route=\/wp\/v2\/posts\/165"}],"collection":[{"href":"https:\/\/aireligion.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aireligion.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aireligion.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aireligion.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=165"}],"version-history":[{"count":1,"href":"https:\/\/aireligion.org\/index.php?rest_route=\/wp\/v2\/posts\/165\/revisions"}],"predecessor-version":[{"id":166,"href":"https:\/\/aireligion.org\/index.php?rest_route=\/wp\/v2\/posts\/165\/revisions\/166"}],"wp:attachment":[{"href":"https:\/\/aireligion.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=165"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aireligion.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=165"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aireligion.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=165"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}