Abstract
Financial statement fraud has proven to be difficult to detect without the assistance of data analytical procedures. In the fraud detection domain, minority class instances cannot be readily found using standard machine learning algorithms. Moreover, incomplete instances or features tend to be removed from investigations, which could lead to greater class imbalance. In this study, a combination of imputation, feature selection and classification is shown to increase the identification of minority samples given severely imbalanced data.
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The current work is being supported by the Department of Science and Technology (DST) and Council for Scientific and Industrial Research (CSIR).
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Moepya, S.O., Nelwamondo, F.V., Twala, B. (2017). Increasing the Detection of Minority Class Instances in Financial Statement Fraud. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_4
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DOI: https://doi.org/10.1007/978-3-319-54430-4_4
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