The study introduces a fraud lexicon and a Balanced Random Forest classifier for detecting fraudulent financial reporting. The classifier, utilizing the fraud lexicon as a feature set, demonstrates strong accuracy in predicting fraud across multiple samples from 2000 to 2017, outperforming random guessing by 40 to 48 percent. The fraud lexicon proves valuable for "bag-of-words" analysis, benefiting researchers, practitioners, auditors, regulators, and investors in enhancing fraud risk assessment procedures.
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