THE IMPACT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ON THE FRAUD DETECTION RATE IN COMPANIES LISTED ON THE STOCK EXCHANGE OF THAILAND
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Abstract
This study aims to 1) examine the differences in fraud detection rates between organizations using artificial intelligence and machine learning (AI/ML) and the average industry fraud detection rate, 2) assess the fraud risk between organizations using AI/ML and those not using AI/ML, and 3) investigate the factors influencing fraud detection rates in publicly listed companies on the Stock Exchange of Thailand. The research sample consisted of 174 executives and internal audit officers of listed companies. The instrument used was a questionnaire, with item-objective congruence (IOC) ranging from 0.78 to 0.92 and reliability values ranging from 0.82 to 0.86. Statistical analyses were conducted to test the hypotheses and evaluate the relationship between the use of AI/ML and fraud detection rates.
The results revealed that 1) organizations using AI/ML had fraud detection rates significantly higher than the industry average at a statistical significance level of 0.01, 2) organizations using AI/ML had lower fraud risk than organizations not using AI/ML at a statistical significance level of 0.01, and 3) the factors significantly affecting fraud detection rates included the use of AI/ML in fraud detection, satisfaction with the use of AI/ML, and work experience.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความทุกเรื่องได้รับการตรวจความถูกต้องทางวิชาการโดยผู้ทรงคุณวุฒิ ทรรศนะและข้อคิดเห็นในบทความ Journal of Global of Perspectives in Humanities and Social Sciences (J-GPHSS) มิใช่เป็นทรรศนะและความคิดของผู้จัดทำจึงมิใช่ความรับผิดชอบของบัณฑิตวิทยาลัย มหาวิทยาลัยราชภัฏวไลยอลงกรณ์ ในพระบรมราชูปถัมภ์ กองบรรณาธิการไม่สงวนสิทธิ์การคัดลอก แต่ให้อ้างอิงแหล่งที่มา
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