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.

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This paper tests the meta-analysis based unified theory of acceptance and use of technology (meta-UTAUT) model to predict the behavioral intentions of organizational users and their use behavior to artificial intelligence (AI) integrated customer relationship management (CRM) systems. Data was collected from 315 organizational users in India. The hypotheses draw on the theoretical underpinnings which have been statistically validated. Results show that CRM quality and satisfaction significantly influences an organization’s employees attitudes and intentions to use AI integrated CRM systems. The compatibility of CRM systems has, however, a limited impact on employees attitudes. The findings, which are aligned with the extended UTAUT model, provide useful insights into organizations and decision-makers for designing AI integrated CRM systems.