Abstract
Various types of fraud exist in almost every country, classified as corruption, asset misappropriation, and financial statement fraud. Occupational fraud, a mixture of corruption and asset misappropriation, has been mostly committed within companies, significantly impacting their economic stability. Interview and questionnaire techniques were employed to collect data from the companies listed on the Zimbabwe stock exchange. Primary data has been collected from 44 respondents through questionnaires. It is found that companies in Zimbabwe are widely using Excel for internal audits. Internal audits play a crucial role in fraud prevention and detection, but there is room for improvement, particularly in leveraging data analytics to enhance effectiveness. To prevent occupational fraud, companies in Zimbabwe should strengthen their internal audit mechanism and leverage data analytics tools such as ACL, IDEA, SQL, Power BI, Tableau, Python, and R. These measures can help detect and prevent fraud at an early stage, safeguarding organizational assets and improving the quality of internal audits. In addition to adopting data analytics tools, companies should focus on integrating advanced fraud detection methodologies, such as machine learning and predictive analytics, into their internal audit systems. Regular auditor training on emerging fraud schemes and digital threats is essential for maintaining effective fraud prevention. Establishing stronger internal controls, periodic audits, and fostering a culture of accountability and transparency within organizations can further mitigate the risk of occupational fraud. Furthermore, collaboration with external auditors can provide an independent assessment, adding extra protection against fraud.
Keywords: Asset misappropriation, Data Analytics, Internal Audit, Occupational Fraud.