No abstract

Purpose – The purpose of this study is to investigate the effectiveness of fraud detection instruments in not-for-profit (NFP) organizations. Not-for-profit organizations rely on trust and volunteer support. They are often small in size and do not have relevant expertise to prevent fraud. Such organizations are more vulnerable to fraud and, consequently, require effective fraud detection instruments. The existing literature on fraud detection is primarily descriptive and does not measure instrument performance. The authors address this research gap and provide a detailed overview of the impact of nine common fraud detection instruments. Design/methodology/approach – Data were obtained from an NFP fraud survey conducted in Australia and New Zealand. A set of contingency tables is produced to explore the relationship between the existence of a specific fraud detection instrument and actual detection of fraud. We also investigate the relationship between organization size and fraud detection strategy. Findings – The findings provide valuable insights into understanding fraud detection mechanisms. Although most fraud detection measures may not lead to more fraud detection, three highly effective instruments emerge, namely, fraud control policies, whistle-blower policies and fraud risk registers. The results also reveal that commonly used fraud detection instruments are not necessarily the most effective. This is true in a significant number of small organizations that appear to be focusing on ineffective fraud detection instruments. Practical implications – Implementation of more effective fraud detection measures will reduce the damage caused to an organization and is highly relevant for practitioners. Originality/value – The results show that differences in the effectiveness of fraud detection instruments in the NFP sector exist. This knowledge is directly applicable by related organizations to reduce fraud damage.

Purpose – The purpose of this paper is to demonstrate the technical feasibility of implementing multi-view visualization methods to assist auditors in reviewing the integrity of high-volume accounting transactions. Modern enterprise resource planning (ERP) systems record several thousands of transactions daily. This makes it difficult to find a few instances of anomalous activities among legitimate transactions. Although continuous auditing and continuous monitoring systems perform substantial analytics, they often produce lengthy reports that require painstaking post-analysis. Approaches that reduce the burden of excessive information are more likely to contribute to the overall effectiveness of the audit process. The authors address this issue by designing and testing the use of visualization methods to present information graphically, to assist auditors in detecting anomalous and potentially fraudulent accounts payable transactions. The strength of the authors ' approach is its capacity for discovery and recognition of new and unexpected insights. Design/methodology/approach – Data were obtained from the SAP enterprise (ERP) system of a real-world organization. A framework for performing visual analytics was developed and applied to the data to determine its usefulness and effectiveness in identifying anomalous activities. Findings – The paper provides valuable insights into understanding the use of different types of visualizations to effectively identify anomalous activities. Research limitations/implications – Because this study emphasizes asset misappropriation, generalizing these findings to other categories of fraud, such as accounts receivable, must be made with caution. Practical implications – This paper provides a framework for developing an automated visualization solution which may have implications in practice. Originality/value – This paper demonstrates the need to understand the effectiveness of visualizations in detecting accounting fraud. This is directly applicable to organizations investigating methods of improving fraud detection in their ERP systems.

The purpose of this study is to examine the impact of the use of an online discussion board as an assessment item on students learning performance in relation to group work based on Social Learning Theory. The study uses survey questionnaires at the beginning and end of semester together with student grade information. The data analysis consists of (1) a regression analysis to explore the relationship between student interaction and performance and (2) a repeated measures ANOVA to explore changes in attitude and perceived encouragement. Student's perceptions at the outset were found to be important as was the use of the online discussion board as a learning tool even when it is not assessed. Further, students' attitude to the online discussion board improved through the semester however the online assessment task did not encourage domestic students to be more engaged in group‐based activities. In contrast, international students were more encouraged to participate in group‐based activities at the end of the semester. The study has implications for online group activities in education.

Fraud is a multi-billion dollar industry that continues to grow annually. Many organizations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper, we adopt a DesignScience methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated by developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: (a) automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud; and (b) visualizations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: (a) a model for proactive fraud detection; (b) methods for visualizing user activities in transaction data; and (c) a stand-alone Monitoring and Control Layer (MCL) based prototype.