Detecting tax evasion: a co-evolutionary approach

Hemberg, Erik; Rosen, Jacob; Warner, Geoff; Wijesinghe, Sanith; O’Reilly, Una-May

doi:10.1007/s10506-016-9181-6

Cited by 29 publications

(13 citation statements)

References 37 publications

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“…For example, some recent applications of AI include the development of self-driving cars and of computers that can beat the world's best chess or Jeopardy players. In the context of tax enforcement, recent research has demonstrated that AI can anticipate particular modes of tax evasion, by identifying those tax schemes that taxpayers and their advisers employ to engage in tax evasion, as demonstrated and analyzed by Warner et al (2015), Hashimzade et al (2015), Hemberg et al (2016), and . AI is especially helpful in seeing detecting patterns of individual use of tax code regulations that can then be used collectively to create a sophisticated -and illegal -tax avoidance scheme.…”

Section: Toward Less Tax Evasionmentioning

confidence: 99%

Tax evasion, technology, and inequality

Alm

2021

Econ Gov

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Ensuring compliance with the tax laws is an enduring challenge for all governments. However, the methods by which governments enforce the tax laws, and by which individuals and firms evade their taxes, change over time, due at least in part to changing technology. In this paper I examine how changing technology, especially changes driven by the transformation of information into digital formats for use by computers, seems likely to affect tax evasion in the years ahead. I argue that many of these changes in technology will improve the ability of governments to decrease tax evasion, mainly by increasing the flow of information to governments. However, I also argue that these changes in technology will open up new avenues by which some individuals and some firms can evade (and avoid) taxes. At this point it is unclear which trend will dominate, so that the effects of technology on the overall level of tax evasion is uncertain. Even so, I believe that the distributional effects of these technological changes are more predictable, given the differential effects of technology on the abilities of individuals of different levels and types of income to evade their taxes. Indeed, I argue that changing technology will make evasion increasingly difficult for most taxpayers, especially those subject to employer withholding and third party information reporting, but that evasion will be increasingly viable for a small number of taxpayers, especially very high income taxpayers. Regardless of the overall impact of technology on the level of tax evasion, I conclude that the effects of technology will likely increase economic inequality.

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Section: Toward Less Tax Evasionmentioning

confidence: 99%

Tax evasion, technology, and inequality

Alm

2021

Econ Gov

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“…For example, Google image search is able to quite accurately categorize images. 9 And now there are proposals coming both form academia and private companies for how to use AI to combat terrorism (Aviv 2009;Frenkel 2017), discover illegal immigrants, catch tax evaders (Hemberg et al 2016), etc. Governments are feeding algorithms bulk collected data so that algorithms can make decisions about us.…”

Section: Algorithmic Opacitymentioning

confidence: 99%

The Value of Transparency: Bulk Data and Authoritarianism

Robbins

Henschke

2017

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Transparency is important for liberal democracies; however, the value of transparency is difficult to articulate. In this article we articulate transparency as an instrumental value for providing what we call ensurance and assurance to liberal democratic citizens. Ensurance refers to the property of liberal democracies which prevents it from sliding into authoritarianism and assurance is the property whereby citizens are assured that ensurance exists. Looking at the rise of bulk data collection and use afforded by information communication technologies, this paper focuses on the way that technologies disrupt relations between the state and its citizens, and suggests Value Sensitive Design as a methodology to protect key aspects of liberal democracies.Bulk data collection makes the achieving of ensurance and assurance more difficult due to two types of opacity which arise as a result of the practice: technical opacity-the difficulty for citizens to understand the technology behind bulk data collection; and, algorithmic opacity-opacity which results from properties inherent to algorithms which guide the collection and processing of bulk data. Design requirements will be suggested to respond to the disruptions caused by ICTs between liberal democracies and their citizens which threaten the necessary value for liberal democracies of representativeness.

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“…By choosing a suitable representation of the fraud schema, it is possible to find more sophisticated patterns of reasoning for a fraudulent agent [e.g., by applying algorithmic evolutionary methods (Hemberg et al. 2016 )].…”

Section: Introductionmentioning

confidence: 99%

Manipulation of the Bitcoin market: an agent-based study

et al. 2022

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Fraudulent actions of a trader or a group of traders can cause substantial disturbance to the market, both directly influencing the price of an asset or indirectly by misinforming other market participants. Such behavior can be a source of systemic risk and increasing distrust for the market participants, consequences that call for viable countermeasures. Building on the foundations provided by the extant literature, this study aims to design an agent-based market model capable of reproducing the behavior of the Bitcoin market during the time of an alleged Bitcoin price manipulation that occurred between 2017 and early 2018. The model includes the mechanisms of a limit order book market and several agents associated with different trading strategies, including a fraudulent agent, initialized from empirical data and who performs market manipulation. The model is validated with respect to the Bitcoin price as well as the amount of Bitcoins obtained by the fraudulent agent and the traded volume. Simulation results provide a satisfactory fit to historical data. Several price dips and volume anomalies are explained by the actions of the fraudulent trader, completing the known body of evidence extracted from blockchain activity. The model suggests that the presence of the fraudulent agent was essential to obtain Bitcoin price development in the given time period; without this agent, it would have been very unlikely that the price had reached the heights as it did in late 2017. The insights gained from the model, especially the connection between liquidity and manipulation efficiency, unfold a discussion on how to prevent illicit behavior.

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Detecting tax evasion: a co-evolutionary approach

Cited by 29 publications

References 37 publications

Tax evasion, technology, and inequality

Tax evasion, technology, and inequality

The Value of Transparency: Bulk Data and Authoritarianism

Manipulation of the Bitcoin market: an agent-based study

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