  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.
