Abstract
This study introduces a novel perspective on financial fraud detection by exploring the utility of managers' abnormal tone. To mitigate bias in indicator selection, we implement a feature selection process involving a comprehensive set of 301 indicators, including financial, non-financial, and textual, and various machine learning algorithms. The dataset contains 6077 pairs of fraudulent and non-fraudulent samples in China. Our findings underscore the significance of abnormal tone in fraud detection, establishing it as a prominent factor in the feature selection process. The accuracy outcomes from eight machine learning models further confirm that incorporating abnormal tone can enhance fraud detection performance.
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