Financial Fraud Detection Model Based on Random Forest
Syed Hasnain Alam Kazmi () and
MPRA Paper from University Library of Munich, Germany
Business's accelerated globalization has weakened regulatory capacity of the law and scholars have been paid attention to fraud detection in recent years. In this study, we introduced Random Forest (RF) for financial fraud technique detection and detailed features selection, variables’ importance measurement, partial correlation analysis and Multidimensional analysis. The results show that a combination of eight variables has the highest accuracy. The ratio of debt to equity (DEQUTY) is the most important variable in the model. Moreover, we applied four statistic methodologies, including parametric and non-parametric models to construct detection models and concluded that Random Forest has the highest accuracy and the non-parametric models have higher accuracy than non-parametric models. However, Random Forest can improve the detection efficiency significantly and have an important practical implication.
Keywords: Financial Fraud Detection; Random Forest; Ratio of debt to equity; Partial Correlation Analysis; Statistic methodologies; Parametric models (search for similar items in EconPapers)
JEL-codes: C1 G00 G17 M21 (search for similar items in EconPapers)
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Published in International Journal of Economics and Finance 7.7(2015): pp. 178-188
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:65404
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