Fraud detection skills of Thai Gen Z accountants: The roles of digital competency, data science literacy and diagnostic skills

Abstract

The issue of accounting fraud presents a significant challenge within the business sector, prompting an increase in scholarly investigations across various contexts. Despite this growing interest, research specifically addressing the Thai context has remained scarce. Thus, this quantitative study aimed to bridge this gap by assessing the proficiency of Thai Gen Z accountants in detecting accounting fraud, with a particular emphasis on their digital, data science, and diagnostic skills. The study collected data from 150 participants using a structured survey questionnaire distributed to licensed accountants affiliated with the Thailand accounting program. It adopted a theoretical framework inspired by social learning theory and information processing theory to examine both direct and mediated relationships among the key variables under investigation. The results were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine these relationships. The results showed that digital competency have significant direct effects on the fraud detection skills, with diagnostic skills playing a key role in the process. The study revealed that digital competency not only furnishes accountants with necessary technological expertise but also bolsters their analytical skills, which are vital for identifying fraudulent activities. Likewise, data science literacy—encompassing skills in predictive analytics, big data management, and data insight communication—significantly enhances accountants' capacity to identify and understand fraudulent patterns. The emergent role of diagnostic skills as a key intermediary emphasizes the importance of comprehensive training programs that foster both technical prowess and critical analytical thinking.