The Impact of AI-Driven Risk Management on Earnings Management: Empirical Evidence from Jordan’s Industrial Sector

Abstract

This article analyzes the influence of AI-managed risks on employment manipulation (EM) within a Jordanian industrial firm. To guarantee comprehensive representation, 312 interviews were conducted with industry professionals possessing diverse responsibilities, organizational sizes, and demographic backgrounds via a standardized survey from March 2025 to May 2025. To validate and ensure the reliability of the findings, the research employed PLS-SEM with an adequate sample size. The results indicate that the proper integration of AI-driven risk management into operations substantially influences earnings management. The findings indicate that risk management plays a pivotal role in influencing financial reporting and discretionary accounting practices as an intermediary factor in the indirect relationship between AI deployment and earnings management outcomes. The function of AI in the regulation and implementation of earnings commodities strategies, a topic that has received less empirical focus in emerging nations like Jordan, addresses a significant gap in the literature. The findings indicate that AI improves operational efficiency, mitigates the risks of financial fraud, and strengthens regulatory compliance, impacting the three phases of planning, execution, and monitoring. These results increase the integration of theory and practice by providing distinct insights into the deployment of AI to influence earnings management, improve transparency, and ensure long-term financial sustainability in industrial firms.