A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

Abstract

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.

Data Mining and Knowledge Extraction in the Risk Based Insurance Audit: A Case Study (City of Tehran)

“…Shin et al, conducted an investigation in Korea, using the decision tree, into insurance abuses of outpatients in various clinics [12]. Sharma and Pangarahi, assessed data mining methods, such as neural network, Bayesian network, and decision trees in financial fraud detection [13] ,Hung, considering non-financial factors in order to produce a more robust detection, identified financial frauds using the SVM algorithm [14].…”

“…Naive Bayes utilizes the Bayes conditional rules [13]. Assuming that there are C classes for the new X sample, the classified section foretells that X belongs to the class that has the highest posterior conditional probability on X.…”

A Data Mining Framework for Prevention and Detection of Financial Statement Fraud

“…The first comprising the six data mining application classes of classification, clustering, prediction, outlier detection, regression, and visualization supported by second layer which consists of a set of algorithmic approaches to extract the relevant relationships in the data. Recently, Sharma & Panigrahi [27] propose a data mining framework for detection of financial fraud. The review of the academic research till date revels that most of the research has been done in the field of identification and detection of financial statement fraud.…”

Data Mining in Banking and Its Applications-a Review

“…Even though these statements may have been audited, these kinds of frauds are hard to detect using normal auditing procedures. Classification techniques based on neural network, regression and decision tree are used for classifying fraudulent ratios in the statements from the nonfraudulent data (Sharma and Panigrahi, 2012).…”