Tax Non-compliance Detection Using Co-evolution of Tax Evasion Risk and Audit Likelihood

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@InProceedings{Hemberg:2015:ICAIL,
  author =       "Erik Hemberg and Jacob Rosen and Geoff Warner and 
                 Sanith Wijesinghe and Una-May O'Reilly",
  title =        "Tax Non-compliance Detection Using Co-evolution of Tax
                 Evasion Risk and Audit Likelihood",
  booktitle =    "Proceedings of the 15th International Conference on
                 Artificial Intelligence and Law, ICAIL-2015",
  year =         "2015",
  editor =       "Katie Atkinson and Ted Sichelman",
  pages =        "79--88",
  address =      "San Diego, USA",
  publisher_address = "New York, NY, USA",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, coevolution, auditing policy, innovative
                 applications, tax evasion",
  isbn13 =       "978-1-4503-3522-5",
  URL =          "http://doi.acm.org/10.1145/2746090.2746099",
  DOI =          "doi:10.1145/2746090.2746099",
  acmid =        "2746099",
  size =         "10 pages",
  abstract =     "We detect tax law abuse by simulating the co-evolution
                 of tax evasion schemes and their discovery through
                 audits. Tax evasion accounts for billions of dollars of
                 lost income each year. When the IRS pursues a tax
                 evasion scheme and changes the tax law or audit
                 procedures, the tax evasion schemes evolve and change
                 into undetectable forms. The arms race between tax
                 evasion schemes and tax authorities presents a serious
                 compliance challenge. Tax evasion schemes are sequences
                 of transactions where each transaction is individually
                 compliant. However, when all transactions are combined
                 they have no other purpose than to evade tax and are
                 thus non-compliant. Our method consists of an ownership
                 network and a sequence of transactions, which outputs
                 the likelihood of conducting an audit, and requires no
                 prior tax return or audit data. We adjust audit
                 procedures for a new generation of evolved tax evasion
                 schemes by simulating the gradual change of tax evasion
                 schemes and audit points, i.e. methods used for
                 detecting non-compliance. Additionally, we identify,
                 for a given audit scoring procedure, which tax evasion
                 schemes will likely escape auditing. The approach is
                 demonstrated in the context of partnership tax law and
                 the Installment Bogus Optional Basis tax evasion
                 scheme. The experiments show the oscillatory behaviour
                 of a co-adapting system and that it can model the
                 co-evolution of tax evasion schemes and their
                 detection.",
  notes =        "Presented at GI COW45
                 http://crest.cs.ucl.ac.uk/cow/45/

                 Also known as \cite{Hemberg:2015:TND:2746090.2746099}",
}

Genetic Programming entries for Erik Hemberg Jacob Rosen Geoff Warner Sanith Wijesinghe Una-May O'Reilly

Citations