Enhancing Fraud Risk Detection in Auditing: An Empirical Analysis of Beneish and Roxas Models Applied to Marketing Companies in Ukraine

Financial statement fraud remains one of the most persistent challenges confronting the auditing profession and the global financial reporting system.Despite the continuous evolution of auditing standards, regulatory oversight mechanisms, and corporate governance frameworks, the manipulation of accounting information continues to occur in various sectors of the economy.This study examines the effectiveness of analytical procedures in auditing as a tool for predicting the risk of financial statement manipulation within Ukrainian marketing companies during the period 2023-2024.The research focuses on the application of two widely recognized analytical models used in forensic accounting and fraud detection: the Beneish M-Score model and the Roxas model.Both models rely on financial ratio analysis and statistical techniques to identify unusual patterns and deviations in financial statements that may signal potential manipulation or earnings management practices.Using empirical data from a sample of fifty Ukrainian marketing companies, the study evaluates the ability of these models to detect anomalies in financial reporting.Particular attention is given to the structural characteristics of marketing companies, including their limited reliance on fixed assets, the prevalence of intangible resources, and the frequent use of revenue recognition mechanisms linked to service-based Enhancing Fraud Risk Detection in Auditing: An Empirical Analysis of Beneish and Roxas Models Applied to Marketing Companies in Ukraine ---Nina Poyda-Nosyk; Serhii Lehenchuk; Victoriia Makarovych; Iryna Polishchuk; Tetiana Zavalii transactions.These characteristics may simplify operational processes but also increase the vulnerability of financial statements to manipulation.The comparative analysis reveals notable differences in the predictive outcomes of the Beneish and Roxas models.While both approaches identify firms that may be exposed to manipulation risks, the Beneish model demonstrates a stronger diagnostic capacity due to its broader integration of financial indicators, including income, expense, asset, and leverage relationships.The findings suggest that the Ukrainian marketing sector exhibits an elevated risk of financial reporting manipulation, a situation that may be intensified by the economic instability and operational uncertainties associated with the ongoing martial law conditions in the country.This research contributes to the growing body of literature on forensic accounting and fraud detection by validating the applicability of classical analytical models in emerging market environments experiencing economic and institutional turbulence.Furthermore, the results provide practical implications for auditors, regulators, and financial analysts seeking to enhance financial oversight and improve the reliability of corporate reporting through the integration of analytical procedures within the audit process.