A Comprehensive Survey of Data Mining-Based Accounting-Fraud Detection Research

This survey paper categorizes, compares, and summarizes the data set, algorithm and performance measurement in almost all published technical and review articles in automated accounting fraud detection. Most researches regard fraud companies and non-fraud companies as data subjects, Eigenvalue cover...

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Bibliographic Details
Published in:2010 International Conference on Intelligent Computation Technology and Automation Vol. 1; pp. 50 - 53
Main Author: Shiguo Wang
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2010
Subjects:
Accounting-fraud detection
Algorithm
Automation
Classifier Evaluation
Computer crime
Data mining
Eigenvalues and eigenfunctions
Neural networks
Paper technology
Regression analysis
Risk management
Technology management
Testing
ISBN:9781424472796, 1424472792
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Summary:This survey paper categorizes, compares, and summarizes the data set, algorithm and performance measurement in almost all published technical and review articles in automated accounting fraud detection. Most researches regard fraud companies and non-fraud companies as data subjects, Eigenvalue covers auditor data, company governance data, financial statement data, industries, trading data and other categories. Most data in earlier research were auditor data; Later research establish model by using sharing data and public statement data. Company governance data have been widely used. It is generally believed that ratio data is more effective than accounting data; Seldom research on time Series Data Mining were conducted. The retrieved literature used mining algorithms including statistical test, regression analysis, neural networks, decision tree, Bayesian network, and stack variables etc.. Regression Analysis is widely used on hiding data. Generally the detecting effect and accuracy of NN are superior to regression model. General conclusion is that model detecting is better than auditor detecting rate without assisting. There is a need to introduce other algorithms of no-tag data mining. Owing to the small size of fraud samples, some literature reached conclusion based on training samples and may overestimated the effect of model.
ISBN:9781424472796
1424472792
DOI:10.1109/ICICTA.2010.831