Beneish M-score and Altman Z-score as a catalyst for corporate fraud detection.

Purpose: The increasing incidence of fraudulent financial reporting by firms in recent years raises concerns about investors' confidence in capital markets. Academicians and industry practitioners adopt diverse risk management techniques to detect fraudulent reporting of financial statements. This paper aims to determine the effectiveness of the Beneish M-score and Altman Z-score models for the early detection of material misstatements at Comscore, Inc., a media analytics firm in the United States of America. Design/methodology/approach: The financial statements of Comscore Inc. from 2012 to 2018 were analyzed with the primary objective of early fraud detection by employing the Beneish M-score and the Altman Z-score. Findings: The study's outcomes indicate that the Beneish M-score is less predictable in fraud detection compared to the Altman Z-score. The study results did not confirm the efficacy of the Beneish model in predicting fraudulent financial statements. The study concludes that the choice of forensic tool greatly influences fraud detection outcomes. Practical Implication: The research findings can guide the policy decision-making of investors, financial auditors, and forensic auditors as this study provides some evidence of the effectiveness of forensic tools in the detection of financial statement fraud in corporate entities. Originality/value: This is the first study to apply these two widely used tools to the most recent big corporate scandal: Comscore, Inc. [ABSTRACT FROM AUTHOR]