Proposing the Throughput Model as Potential Algorithmic Pathways for Employing AI Machine Learning Technologies for Fraud Risk Assessments

dc.contributor.authorShammakhi, Badriya
dc.contributor.authorRodgers, Waymond
dc.contributor.authorEsplin, Adam
dc.contributor.authorTudor, Adriana
dc.contributor.authorYan, Jie
dc.date.accessioned2024-12-26T21:07:33Z
dc.date.available2024-12-26T21:07:33Z
dc.date.issued2025-01-07
dc.description.abstractThis paper proposes and tests the Throughput Model as a potential algorithmic pathway for employing AI machine learning technologies for fraud risk assessments in auditing. The Throughput Model breaks up decision-making into four dominant concepts: Perception (P), Information (I), Judgment (J), and Decision Choice (D). This study focuses on the I→P→J→D pathway and proposes that AI machine learning algorithms following this pathway will significantly influence an auditor’s decision choice in fraud risk assessments. An exploratory study tests the effectiveness of employing I→P→J→D as a potential algorithmic pathway for fraud risk assessments. Our results show that there is a significant positive relationship between Perception and Judgment and another direct positive relation between Judgment and Decision. These findings suggest that the Throughput Model algorithms are an effective decision aid in consideration of the fraud risk factors in auditing. We also discuss the study's implications and provide guidelines for future research.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.459
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other4ef09736-0701-4d73-b7f5-0d7976393747
dc.identifier.urihttps://hdl.handle.net/10125/109303
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtificial Intelligence-based Assistants and Platforms
dc.subjectalgorithmic pathway, artificial intelligence, auditing, fraud risk factors, machine learning, throughput model
dc.titleProposing the Throughput Model as Potential Algorithmic Pathways for Employing AI Machine Learning Technologies for Fraud Risk Assessments
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage3810

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