Detecting financial statement fraud using dynamic ensemble machine learning

Our study uses Machine learning to develop an advanced fraud detection model that can detect fraudulent firms. We build our model using raw financial and non-financial variables following prior literature. In addition, we introduce the Dynamic Ensemble Selection algorithm to the fraud detection lite...

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Bibliographic Details
Published in:International review of financial analysis 2023-10, Vol.89, p.102827, Article 102827
Main Authors: Achakzai, Muhammad Atif Khan, Peng, Juan
Format: Article
Language:English
Subjects:
Detection
Dynamic ensemble selection
Fraud
Machine learning
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Summary:Our study uses Machine learning to develop an advanced fraud detection model that can detect fraudulent firms. We build our model using raw financial and non-financial variables following prior literature. In addition, we introduce the Dynamic Ensemble Selection algorithm to the fraud detection literature, which combines individual classifiers dynamically to make a final prediction. Using several performance evaluation metrics, we find that our model can outperform several machine learning models used in recent studies. •We develop an advanced Machine Learning Dynamic Ensemble Selection (DES) model to detect financial statement fraud.•The Fraud detection model uses raw financial and non-financial variables.•The DES model outperforms several Machine learning models used in recent fraud detection literature.•The DES methodology can also be extended to other settings where improving the predictive performance is vital.
ISSN:1057-5219
1873-8079
DOI:10.1016/j.irfa.2023.102827