Shammakhi, BadriyaRodgers, WaymondEsplin, AdamTudor, AdrianaYan, Jie2024-12-262024-12-262025-01-07978-0-9981331-8-84ef09736-0701-4d73-b7f5-0d7976393747https://hdl.handle.net/10125/109303This 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.10Attribution-NonCommercial-NoDerivatives 4.0 InternationalArtificial Intelligence-based Assistants and Platformsalgorithmic pathway, artificial intelligence, auditing, fraud risk factors, machine learning, throughput modelConference Paper