Moepya, SOAkhoury, SSNelwamondo, Fulufhelo V2015-08-192015-08-192014-12Moepya, S.O, Akhoury, S.S and Nelwamondo, F.V. 2014. Applying cost-sensitive classification for financial fraud detection under high class-imbalance. In: 2014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, 14 December 2014http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7022596http://hdl.handle.net/10204/80672014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, 14 December 2014. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's websiteIn recent years, data mining techniques have been used to identify companies who issue fraudulent financial statements. However, most of the research conducted thus far use datasets that are balanced. This does not always represent reality, especially in fraud applications. In this paper, we demonstrate the effectiveness of cost-sensitive classifiers to detect financial statement fraud using South African market data. The study also shows how different levels of cost affect overall accuracy, sensitivity, specificity, recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers.enFinancial statement fraudData miningHigh class-imbalanceCost-sensitive classificationConference PresentationMoepya, S., Akhoury, S., & Nelwamondo, F. V. (2014). Applying cost-sensitive classification for financial fraud detection under high class-imbalance. IEEE. http://hdl.handle.net/10204/8067Moepya, SO, SS Akhoury, and Fulufhelo V Nelwamondo. "Applying cost-sensitive classification for financial fraud detection under high class-imbalance." (2014): http://hdl.handle.net/10204/8067Moepya S, Akhoury S, Nelwamondo FV, Applying cost-sensitive classification for financial fraud detection under high class-imbalance; IEEE; 2014. http://hdl.handle.net/10204/8067 .TY - Conference Presentation AU - Moepya, SO AU - Akhoury, SS AU - Nelwamondo, Fulufhelo V AB - In recent years, data mining techniques have been used to identify companies who issue fraudulent financial statements. However, most of the research conducted thus far use datasets that are balanced. This does not always represent reality, especially in fraud applications. In this paper, we demonstrate the effectiveness of cost-sensitive classifiers to detect financial statement fraud using South African market data. The study also shows how different levels of cost affect overall accuracy, sensitivity, specificity, recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers. DA - 2014-12 DB - ResearchSpace DP - CSIR KW - Financial statement fraud KW - Data mining KW - High class-imbalance KW - Cost-sensitive classification LK - https://researchspace.csir.co.za PY - 2014 T1 - Applying cost-sensitive classification for financial fraud detection under high class-imbalance TI - Applying cost-sensitive classification for financial fraud detection under high class-imbalance UR - http://hdl.handle.net/10204/8067 ER -