A Comparative Investigation of Fraud Detection Models in the Coffee Industry: A Case Study of Luckin Coffee and its Competitors

This research paper explores the efficacy of financial distress detection models, focusing on the Altman Z-Score, Ohlson O-Score, and Beneish M-Score metrics as a fraud detection tools in detecting corporate financial fraud, using Luckin Coffee Incorporation as a case study. The study highlights the superior predictability tools at initial stages for a firm indulging in fraudulent practices, shedding light on the complexities of financial misrepresentation in the corporate landscape. By providing a comparative analysis of Luckin Coffee with its competitors, namely Starbucks Corporation and McDonald’s Corporation for 5 years from 2018 to 2022, the research aims to enhance transparency and accountability within corporate finance. The findings of this study highlight that Z-Score and M-Score are perfect tools for fraud detection, whereas O-Score gives contradictory result and this research is expected to assist researchers, auditors, and investors in identifying and mitigating financial fraudulent activities, thereby strengthening investor confidence in the market, and safeguarding against potential bankruptcy risks associated with misleading financial reporting practices.