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Identifying financial statement fraud with decision rules obtained from Modified Random Forest

Identifying financial statement fraud with decision rules obtained from Modified Random Forest Financial statement fraud (FSF) committed by companies implies the current status of the companies may not be healthy. As such, it is important to detect FSF, since such companies tend to conceal bad information, which causes a great loss to various stakeholders. Thus, the objective of the paper is to propose a novel approach to building a classification model to identify FSF, which shows high classification performance and from which human-readable rules are extracted to explain why a company is likely to commit FSF.Design/methodology/approachHaving prepared multiple sub-datasets to cope with class imbalance problem, we build a set of decision trees for each sub-dataset; select a subset of the set as a model for the sub-dataset by removing the tree, each of whose performance is less than the average accuracy of all trees in the set; and then select one such model which shows the best accuracy among the models. We call the resulting model MRF (Modified Random Forest). Given a new instance, we extract rules from the MRF model to explain whether the company corresponding to the new instance is likely to commit FSF or not.FindingsExperimental results show that MRF classifier outperformed the benchmark models. The results also revealed that all the variables related to profit belong to the set of the most important indicators to FSF and that two new variables related to gross profit which were unapprised in previous studies on FSF were identified.Originality/valueThis study proposed a method of building a classification model which shows the outstanding performance and provides decision rules that can be used to explain the classification results. In addition, a new way to resolve the class imbalance problem was suggested in this paper. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Data Technologies and Applications Emerald Publishing

Identifying financial statement fraud with decision rules obtained from Modified Random Forest

Data Technologies and Applications , Volume 54 (2): 21 – Jun 2, 2020

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2514-9288
DOI
10.1108/dta-11-2019-0208
Publisher site
See Article on Publisher Site

Abstract

Financial statement fraud (FSF) committed by companies implies the current status of the companies may not be healthy. As such, it is important to detect FSF, since such companies tend to conceal bad information, which causes a great loss to various stakeholders. Thus, the objective of the paper is to propose a novel approach to building a classification model to identify FSF, which shows high classification performance and from which human-readable rules are extracted to explain why a company is likely to commit FSF.Design/methodology/approachHaving prepared multiple sub-datasets to cope with class imbalance problem, we build a set of decision trees for each sub-dataset; select a subset of the set as a model for the sub-dataset by removing the tree, each of whose performance is less than the average accuracy of all trees in the set; and then select one such model which shows the best accuracy among the models. We call the resulting model MRF (Modified Random Forest). Given a new instance, we extract rules from the MRF model to explain whether the company corresponding to the new instance is likely to commit FSF or not.FindingsExperimental results show that MRF classifier outperformed the benchmark models. The results also revealed that all the variables related to profit belong to the set of the most important indicators to FSF and that two new variables related to gross profit which were unapprised in previous studies on FSF were identified.Originality/valueThis study proposed a method of building a classification model which shows the outstanding performance and provides decision rules that can be used to explain the classification results. In addition, a new way to resolve the class imbalance problem was suggested in this paper.

Journal

Data Technologies and ApplicationsEmerald Publishing

Published: Jun 2, 2020

Keywords: Financial statement fraud; Random forest; Decision rules; Feature importance; Machine learning; Predictive model

References