With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
M arketing literature states that it is more costly to engage a new customer than to retain an existing loyal customer. Churn prediction models are developed by academics and practitioners to effectively manage and control customer churn in order to retain existing customers. As churn management is an important activity for companies to retain loyal customers, the ability to correctly predict customer churn is necessary. As the cellular network services market becoming more competitive, customer churn management has become a crucial task for mobile communication operators. This paper proposes a neural network (NN) based approach to predict customer churn in subscription of cellular wireless services. The results of experiments indicate that neural network based approach can predict customer churn with accuracy more than 92%. Further, it was observed that medium sized NNs perform best for the customer churn prediction when different neural network's topologies were experimented.
General TermsPrediction, Neural Networks, Churn M anagement.
PurposeThis study extends the literature on the effectiveness of e-learning by investigating the role of student engagement on perceived learning effectiveness (PLE) in the context of Indian higher education. Further, the impact of personal factors (Internet self-efficacy (ISE)) and environmental factors (information, system and service quality parameters) on various dimensions of student engagement (behavioral, emotional and cognitive) is studied through the lens of social cognitive theory (SCT).Design/methodology/approachAn online management information systems (MIS) course is delivered to a batch of 412 postgraduate students. An online survey was conducted to measure the factors affecting their PLE. In addition to the survey, a summative assessment is conducted to evaluate the students in terms of their marks to assess their achievements (actual learning). Covariance-based structural equation modeling (CB-SEM) is used to validate the developed research model.FindingsIt is discovered that the IS (information system) quality parameters (environmental factors) positively impact PLE. The ISE affects the PLE through the mediating effect of all the dimensions of student engagement. Furthermore, there exists a positive relationship between PLE and student marks.Originality/valueThis study develops a research model using personal and environmental factors to understand PLE through the lens of SCT and then empirically validates it. The psychological process from the students' ISE to the PLE is explained through the mediating effects of various dimensions of engagement. Further, it is found that the PLE is positively related to student marks.
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