Fraud Detection on Financial Statements Using Data Mining Techniques

Murat Cihan Sorkun, Taner Toraman

Research output: Contribution to journalArticleAcademicpeer-review


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.
Original languageEnglish
Article number549
Pages (from-to)132-134
Number of pages3
JournalInternational Journal of Intelligent Systems and Applications in Engineering
Issue number3
Publication statusPublished - 2017
Externally publishedYes


  • machine learning
  • fraud detection
  • financial statements


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