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Using machine learning Meta-Classifiers to detect financial frauds

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  • Achakzai, Muhammad Atif Khan
  • Juan, Peng

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.

Suggested Citation

  • Achakzai, Muhammad Atif Khan & Juan, Peng, 2022. "Using machine learning Meta-Classifiers to detect financial frauds," Finance Research Letters, Elsevier, vol. 48(C).
    • Handle: RePEc:eee:finlet:v:48:y:2022:i:c:s1544612322001866
    DOI: 10.1016/j.frl.2022.102915
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    References listed on IDEAS

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    Cited by:

    1. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).

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    More about this item

    Keywords

    Machine learning; Financial fraud; Meta-Classifiers; Voting-Classifier; Stacked-Classifier;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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