Intelligence Audit Management Software System (IAMSS): An Intelligent System for Audit Risk Detection and Earnings Management

About the Project

Auditors need to evaluate a large amount of financial information in assessing financial reporting risk. However, unusual financial patterns and potential earnings management may not be immediately identified through conventional financial statement reviews. Therefore, there is a need for analytical tools that can help auditors identify potential earnings management indicators and financial reporting warning signs at an earlier stage. In addition, audit firms may provide audit and allowable non-audit services (NAS) to the same client, subject to applicable professional independence requirements (MIA, 2024). While NAS may provide specific knowledge of the client, the joint provision of audit and NAS services may also create potential threats to the auditor’s independence (Knechel et al., 2012; Yu et al., 2026). Therefore, auditors need effective mechanisms to monitor NAS exposure and identify circumstances that require further independence assessment. Other than that, financial information, earnings management indicators, audit fees, NAS information and other audit risk indicators may require separate analysis. The absence of an integrated analytics platform can increase the time required for initial risk assessment and make it difficult for auditors to obtain a comprehensive picture of client risk. To address these needs, an Intelligence Audit Management Software System (IAMSS) is proposed as a web-based audit decision support system that integrates earnings management detection, financial warning signal analysis, and risk indicators related to Non-Audit Services (NAS) into a single analytics platform. IAMSS is designed to transform financial and auditor-related information into interpretable client risk profiles, thereby assisting auditors in identifying areas that may require further professional attention. IAMSS analyzes financial and auditor-related information to identify potential earnings management, financial reporting red flags, and risks associated with NAS, before presenting the results through an interpretable client risk profile. A key feature of this innovation is the integration of accrual-based and real-activity earnings management detection with NAS-related risk assessments, thereby enabling auditors to identify potential financial reporting risks alongside situations that may warrant further evaluation of auditor independence. Specifically, IAMSS incorporates the Modified Jones Model for AEM detection and the Roychowdhury model for REM detection (Dechow et al., 1995; Roychowdhury, 2006). Through an interactive dashboard, IAMSS translates empirical accounting and auditing research into a practical decision-support tool for audit firms and professional auditors, offering the potential to facilitate more structured, efficient, and risk-focused preliminary audit assessments. The system is designed to complement, rather than replace, the auditor's professional judgment. Keywords : Earnings Management, Non-audit Services, Audit Risk Assessment, Financial Reporting Quality, Audit Decision Support Systems.