Skip to main content

Advertisement

SpringerLink
Log in

Detecting tax evasion: a co-evolutionary approach

  • Published:
Artificial Intelligence and Law Aims and scope Submit manuscript

Abstract

We present an algorithm that can anticipate tax evasion by modeling the co-evolution of tax schemes with auditing policies. Malicious tax non-compliance, or evasion, accounts for billions of lost revenue each year. Unfortunately when tax administrators change the tax laws or auditing procedures to eliminate known fraudulent schemes another potentially more profitable scheme takes it place. Modeling both the tax schemes and auditing policies within a single framework can therefore provide major advantages. In particular we can explore the likely forms of tax schemes in response to changes in audit policies. This can serve as an early warning system to help focus enforcement efforts. In addition, the audit policies can be fine tuned to help improve tax scheme detection. We demonstrate our approach using the iBOB tax scheme and show it can capture the co-evolution between tax evasion and audit policy. Our experiments shows the expected oscillatory behavior of a biological co-evolving system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. http://www.irs.gov/uac/Form-1065,-U.S.-Return-of-Partnership-Income.

References

  • Accountancy JO (2015) http://www.journalofaccountancy.com/news/2015/oct/partnership-audit-and-adjustment-rules-201513294.html

  • Allingham MG, Sandmo A (1972) Income tax evasion: a theoretical analysis. J Public Econ 1(3–4):323–338

    Article  Google Scholar 

  • Andrei AL, Comer K, Koehler M (2013) An agent-based model of network effects on tax compliance and evasion. J Econ Psychol 40:119–133

    Article  Google Scholar 

  • Andreoni J, Erard B, Feinstein J (1998) Tax compliance. J Econ Lit 36:818–860

    Google Scholar 

  • Aubert S, Müller JP (2013) Incorporating institutions, norms and territories in a generic model to simulate the management of renewable resources. Artif Intell Law 21(1):47–78

    Article  Google Scholar 

  • Bench-Capon T, Araszkiewicz M, Ashley K, Atkinson K, Bex F, Borges F, Bourcier D, Bourgine P, Conrad JG, Francesconi E et al (2012) A history of ai and law in 50 papers: 25 years of the international conference on ai and law. Artif Intell Law 20(3):215–319

    Article  Google Scholar 

  • Bloomquist K (2011) Tax compliance as an evolutionary coordination game: an agent-based approach. Public Finance Rev 39(1):25–49

    Article  Google Scholar 

  • Bloomquist KM (2006) A comparison of agent-based models of income tax evasion. Soc Sci Comput Rev 24(4):411–425

    Article  Google Scholar 

  • Boer A, van Engers T (2013) Agile: a problem-based model of regulatory policy making. Artif Intell Law 21(4):399–423

    Article  Google Scholar 

  • Bonchi F, Giannotti F, Mainetto G, Pedreschi D (1999) A classification-based methodology for planning audit strategies in fraud detection. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 175–184

  • Buchanan BG, Headrick TE (1970) Some speculation about artificial intelligence and legal reasoning. Stanford Law Review, pp 40–62

  • Davis JS, Hecht G, Perkins JD (2003) Social behaviors, enforcement, and tax compliance dynamics. Account Rev 78(1):39–69

    Article  Google Scholar 

  • DeBarr D, Eyler-Walker Z (2006) Closing the gap: automated screening of tax returns to identify egregious tax shelters. ACM SIGKDD Explor Newsl 8(1):11–16

    Article  Google Scholar 

  • Dechesne F, Di Tosto G, Dignum V, Dignum F (2013) No smoking here: values, norms and culture in multi-agent systems. Artif Intell Law 21(1):79–107

    Article  Google Scholar 

  • de Jong ED, Stanley KO, Wiegand RP (2007) Introductory tutorial on coevolution. In: GECCO (Companion), pp 3133–3157

  • Ficici SG, Bucci A (2007) Advanced tutorial on coevolution. In: GECCO (Companion), pp 3172–3204

  • GAO (2013) Gao-14-453. http://www.youtube.com/watch?v=O8VDUStvxMY

  • GAO (2014a) http://www.gao.gov/assets/670/663185.pdf

  • GAO (2014b) http://www.gao.gov/assets/670/661772.pdf

  • Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-wesley, Boston

    MATH  Google Scholar 

  • Hemberg E, Rosen J, Warner G, Wijesinghe S, O’Reilly UM (2015) Tax non-compliance detection using co-evolution of tax evasion risk and audit likelihood. In: ICAIL

  • Hokamp S, Pickhardt M (2010) Income tax evasion in a society of heterogeneous agents-evidence from an agent-based model. Int Econ J 24(4):541–553

    Article  Google Scholar 

  • Hokamp S, Seibold G (2014) How much rationality tolerates the shadow economy?—an agent-based econophysics approach. In: Advances in social simulation. Springer, Berlin, pp 119–128

  • IRS (2006) The tax gap. http://www.irs.gov/uac/IRS-The-Tax-Gap

  • IRS (2014a) Irs audits. http://www.irs.gov/Businesses/Small-Businesses-&-Self-Employed/IRS-Audits

  • IRS (2014b) Sales and trades of investment property. http://www.irs.gov/publications/p550/ch04.html

  • Jaideep KWHNP, Bjorklund SGTE (2009) Data mining based tax audit selection: a case study from minnesota department of revenue. In: Proceedings of the third international workshop on data mining case studies. ACM, pp 23–35

  • Kallio M, Back B (2011) The self-organizing map in selecting companies for tax audit. In: Emerging Themes in information systems and organization studies. Physica-Verlag HD, pp 347–358

  • Katz DM, Bommarito MJ II (2014) Measuring the complexity of the law: the united states code. Artif Intell Law 22(4):337–374

    Article  Google Scholar 

  • Kingston J, Schafer B, Vandenberghe W (2004) Towards a financial fraud ontology: a legal modelling approach. Artif Intell Law 12(4):419–446

    Article  Google Scholar 

  • Korobow A, Johnson C, Axtell R (2007) An agent–based model of tax compliance with social networks. National Tax J, pp 589–610

  • Li WP, Azar P, Larochelle D, Hill P, Lo AW (2015) Law is code: a software engineering approach to analyzing the united states code. J Bus Technol Law 10:297

    Google Scholar 

  • Lipatov V (2003) Evolution of tax evasion. Technical report, University Library of Munich, Germany

  • Lotzmann U, Möhring M, Troitzsch KG (2013) Simulating the emergence of norms in different scenarios. Artif Intell Law 21(1):109–138

    Article  Google Scholar 

  • May LR (2012) Using link analysis to identify indirect and multi-tiered ownership structures. In: SOI Tax Stats—2012 IRS-TPC research conference

  • McCarty LT (1977) Reflections on“ taxman”: an experiment in artificial intelligence and legal reasoning. Harvard Law Review pp 837–893

  • Mittone L, Patelli P (2000) Imitative behaviour in tax evasion. In: Economic simulations in swarm: agent-based modelling and object oriented programming. Springer, US, pp 133–158

  • Ngai E, Hu Y, Wong Y, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 50(3):559–569

    Article  Google Scholar 

  • Oard DW, Webber W (2013) Information retrieval for e-discovery. Inf Retr 7(2–3):99–237

    Google Scholar 

  • O’Neill M, Ryan C (2003) Grammatical evolution: evolutionary automatic programming in an arbitrary language, vol 4. Springer, Berlin

    Book  MATH  Google Scholar 

  • Pickhardt M, Prinz A (2014) Behavioral dynamics of tax evasion-a survey. J Econ Psychol 40:1–19

    Article  Google Scholar 

  • Pickhardt M, Seibold G (2014) Income tax evasion dynamics: evidence from an agent-based econophysics model. J Econ Psychol 40:147–160

    Article  Google Scholar 

  • Rosen J, Hemberg E, Warner G, Wijesinghe S, O’Reilly UM (2015) Computer aided tax evasion policy analysis: directed search using autonomous agents. In: AAMAS

  • Rostain T, Regan MC (2014) Confidence games: lawyers, accountants, and the tax shelter industry, vol 1. MIT Press, Massachusetts

    Google Scholar 

  • Sartor G, Rotolo A (2013) AI and law. In: Agreement technologies. Springer, Netherlands, pp 199–207

  • Stanley KO, Miikkulainen R (2004) Competitive coevolution through evolutionary complexification. J Artif Intell Res JAIR 21:63–100

    Google Scholar 

  • Surden H (2014) Machine learning and law. Wash L Rev 89:87–217

    Google Scholar 

  • Van V et al (1973) A new evolutionary law. Evol Theory 1:1–30

    Google Scholar 

  • Warner G, Wijesinghe S, Marques U, Badar O, Rosen J, Hemberg E, O’Reilly UM (2014) Modeling tax evasion with genetic algorithms. Econ Gov. doi:10.1007/s10101-014-0152-7

    Google Scholar 

  • Wiegand RP, Potter MA (2006) Robustness in cooperative coevolution. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, pp 369–376

  • Wright Jr D (2013) Financial alchemy: how tax shelter promoters use financial products to bedevil the irs (and how the irs helps them). Ariz State Law J 45(2)

  • Zaklan G, Lima FW, Westerhoff F (2008) Controlling tax evasion fluctuations. Phys A Stat Mech Appl 387(23):5857–5861

    Article  Google Scholar 

  • Zaklan G, Westerhoff F, Stauffer D (2009) Analysing tax evasion dynamics via the ising model. J Econ Interact Coord 4(1):1–14

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Massachusetts Institute of Technology, Computer Science and AI Lab, 32 Vassar Street, Cambridge, MA, 02139, USA

    Erik Hemberg & Una-May O’Reilly

  2. The MITRE Corporation, 1550 Westbranch Drive, McLean, VA, 22102, USA

    Jacob Rosen, Geoff Warner & Sanith Wijesinghe

Authors
  1. Erik Hemberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jacob Rosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Geoff Warner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sanith Wijesinghe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Una-May O’Reilly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanith Wijesinghe.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hemberg, E., Rosen, J., Warner, G. et al. Detecting tax evasion: a co-evolutionary approach. Artif Intell Law 24, 149–182 (2016). https://doi.org/10.1007/s10506-016-9181-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10506-016-9181-6

Keywords

Search

Navigation