Fraud Detection on Financial Statements Using Data Mining Techniques

Authors

DOI:

https://doi.org/10.18201/ijisae.2017531428

Keywords:

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

Abstract

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.

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Author Biographies

Murat Cihan Sorkun, Galatasaray University Idea Teknoloji Çözümleri

Murat Cihan Sorkun received the B.S. degree in Computer Engineering from Istanbul Technical University, in 2012. He has been studying M.S. at Galataray University Computer Enginnering Department since 2014. He has been working as R&D Engineer in Idea Teknoloji Çözümleri since August 2015.

Taner Toraman, Idea Teknoloji Çözümleri

Taner Toraman received the B.B.A. degree in Business Adminstration from Istanbul University, in 2001. He has been working as R&D Project Manager in Idea Teknoloji Çözümleri, since April 2015. He started his career at Consulta as an auditor and worked for 11 years. When he transferred to Idea Teknolojileri, he was working as a lead auditor at Consulta.

References

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Published

29.09.2017

How to Cite

Sorkun, M. C., & Toraman, T. (2017). Fraud Detection on Financial Statements Using Data Mining Techniques. International Journal of Intelligent Systems and Applications in Engineering, 5(3), 132–134. https://doi.org/10.18201/ijisae.2017531428

Issue

Vol. 5 No. 3 (2017)

Section

Research Article

License

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