Detecting financial statement fraud using dynamic ensemble machine learning

Muhammad Atif Khan Achakzai and Juan Peng

International Review of Financial Analysis, 2023, vol. 89, issue C

Abstract: 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.

Keywords: Fraud; Detection; Dynamic ensemble selection; Machine learning (search for similar items in EconPapers)
JEL-codes: C52 C53 C61 D83 M41 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:89:y:2023:i:c:s1057521923003435

DOI: 10.1016/j.irfa.2023.102827

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