Predictive analytics and the targeting of audits

Authored by Nigar Hashimzade, Gareth D Myles, Matthew D Rablen

Date Published: 2016

DOI: 10.1016/j.jebo.2015.11.009

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

The literature on audit strategies has focused on random audits or on audits conditioned only on income declaration. In contrast, tax authorities employ the tools of predictive analytics to identify taxpayers for audit, with a range of variables used for conditioning. The paper explores the compliance and revenue consequences of the use of predictive analytics in an agent-based model that draws upon a behavioral approach to tax compliance. The taxpayers in the model form subjective beliefs about the probability of audit from social interaction, and are guided by a social custom that is developed from meeting other taxpayers. The belief and social custom feed into the occupational choice between employment and two forms of self-employment. It is shown that the use of predictive analytics yields a significant increase in revenue over a random audit strategy. (C) 2015 Elsevier B.V. All rights reserved.

Tags

Agent-based model probability Risk tax evasion Social interactions expected utility Economy Prospect-theory Taxation Anticipated utility