Fraud Detection on Financial Statements Using Data Mining Techniques

Murat Cihan Sorkun, Taner Toraman


This study explores the use of data mining methods to detect fraud for on e-ledgers through financial statements. For this purpose, data set were produced by rule-based control application using 72 sample e-ledger and error percentages were calculated and labeled. The financial statements created from the labeled e-ledgers were trained by different data mining methods on 9 distinguishing features. In the training process, Linear Regression, Artificial Neural Networks, K-Nearest Neighbor algorithm, Support Vector Machine, Decision Stump, M5P Tree, J48 Tree, Random Forest and Decision Table were used. The results obtained are compared and interpreted.


Data mining, fraud detection, financial statements, e-ledger, machine learning

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Submitted: 2017-03-23 13:37:54
Published: 2017-09-29 16:13:25
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