Using machine learning Meta-Classifiers to detect financial frauds

Muhammad Atif Khan Achakzai and Peng Juan

Finance Research Letters, 2022, vol. 48, issue C

Abstract: We develop Meta-Classifiers to detect financial frauds by combining several accurate and diverse stand-alone classifiers. Our results suggest that the Meta-Classifiers developed in our study can outperform the best stand-alone classifiers to detect fraudulent firms. We believe developing Meta-Classifiers can be a helpful technique to improve the predictive performance of models. Moreover, the methodology used to develop effective Meta-Classifiers in this study can also be replicated in other prediction related studies.

Keywords: Machine learning; Financial fraud; Meta-Classifiers; Voting-Classifier; Stacked-Classifier (search for similar items in EconPapers)
JEL-codes: C52 C53 M41 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001866

DOI: 10.1016/j.frl.2022.102915

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