Data analytics (ab) use in healthcare fraud audits

Jared Koreff, Martin Weisner and Steve G. Sutton

International Journal of Accounting Information Systems, 2021, vol. 42, issue C

Abstract: This study explores how government-adopted audit data analytic tools promote the abuse of power by auditors enabling politically sensitive processes that encourage industry-wide normalization of behavior. In an audit setting, we investigate how a governmental organization enables algorithmic decision-making to alter power relationships to effect organizational and industry-wide change. While prior research has identified discriminatory threats emanating from the deployment of algorithmic decision-making, the effects of algorithmic decision-making on inherently imbalanced power relationships have received scant attention. Our results provide empirical evidence of how systemic and episodic power relationships strengthen each other, thereby enabling the governmental organization to effect social change that might be too politically prohibitive to enact directly. Overall, the results suggest that there are potentially negative effects caused by the use of algorithmic decision-making and the resulting power shifts, and these effects create a different view of the level of purported success attained through auditor use of data analytics.

Keywords: Algorithmic decision-making; Data analytics; Government auditors; Healthcare; Medical fraud detection; Power (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijoais:v:42:y:2021:i:c:s1467089521000257

DOI: 10.1016/j.accinf.2021.100523

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