Fraud Detection on Financial Statements Using Data Mining Techniques
| Autor: | Murat Cihan Sorkun, Taner Toraman |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
fraud detection
Computer science financial statements Sample (statistics) 02 engineering and technology Machine learning computer.software_genre C4.5 algorithm e-ledger Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Data mining 0505 law Finance Artificial neural network business.industry 05 social sciences 020206 networking & telecommunications Computer Graphics and Computer-Aided Design Random forest Support vector machine Tree (data structure) ComputingMethodologies_PATTERNRECOGNITION machine learning Control and Systems Engineering 050501 criminology Decision stump Artificial intelligence business Decision table computer Information Systems |
| Zdroj: | International Journal of Intelligent Systems and Applications in Engineering; Vol. 5 No. 3 (2017); 132-134 |
| ISSN: | 2147-6799 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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