Browsing by Author "Munasinghe, S.T.D."

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    Uses of Technology on Financial Statement Fraud Detection
    (Department of Accountancy, Faculty of Commerce and Management Studies, University of Kelaniya Sri Lanka, 2023) Munasinghe, S.T.D.; Munasinghe, M.A.T.K.
    This study explores how technology is used by audit professionals in major consulting firms, specifically focusing on KPMG, EY, and Deloitte, and its influence on detecting financial statement fraud. The research aims to grasp the practical application, advantages, and challenges related to Computer-Assisted Audit Tools (CAATs). Objectives include understanding software usage, evaluating perceived benefits, and exploring the impact of performance expectancy, facilitating conditions, and technological challenges on financial fraud detection. To gather data, 131 audit professionals were surveyed using a structured questionnaire, and the data were analyzed with SPSS 27. Demographic analysis reveals a significant representation from EY, and all respondents possess basic information technology skills. Software usage is diverse, with Microsoft Excel being the most frequently used software, while ACL, Power BI, IDEA, and In-House Applications show varying usage frequencies. Respondents widely agree on the benefits of CAATs, such as cost reduction, timely task completion, increased accuracy, improved staff performance, and effective fraud detection. Preliminary Testing confirmed the validity of data, and regression analysis showed significant impacts of software types, perceived benefits, facilitating conditions, and technological challenges on fraud detection, while Performance Expectancy had minimal influence. Key findings underscore the importance of software types, perceived benefits, facilitating conditions, and technological challenges in improving fraud detection. The study emphasizes the necessity for tailored approaches to technology adoption in audit practices. In summary, this research contributes valuable insights, offering guidance to auditors and firms in maximizing the use of technology for efficient financial statement fraud detection.