Presenting an Intelligent Model for Detecting Real and Accrual-Based Earnings Management Using Data Mining and Analysis of Managers’ Behavioral Patterns
Keywords:
Real earnings management, accrual-based earnings management, financial data mining, managerial behavioral patterns, intelligent auditing, financial reporting qualityAbstract
This study aimed to present an intelligent model for detecting real and accrual-based earnings management by integrating financial indicators, data mining techniques, and behavioral patterns of financial managers. This qualitative exploratory study was conducted in Tehran. The participants included 22 financial managers, independent auditors, internal auditors, audit committee members, financial analysts, and financial data-mining specialists selected through purposive and snowball sampling. Data were collected exclusively through semi-structured interviews, and the interview process continued until theoretical saturation was achieved. After transcription, the data were analyzed through open, axial, and selective coding. NVivo software was used to organize interview data, classify codes, develop categories, compare themes, and visualize conceptual relationships among the extracted components. Data analysis led to the identification of five main categories: financial and operational indicators of earnings management detection, behavioral and motivational patterns of managers, data-mining infrastructure and intelligent model architecture, corporate governance and internal control mechanisms, and explainability and trustworthiness of algorithmic outputs. The participants emphasized that accrual-based earnings management is mainly reflected in discretionary accruals, accounting estimates, provisions, and revenue-recognition timing, whereas real earnings management appears through abnormal operational decisions such as discretionary expense reduction, unusual sales discounts, overproduction, and manipulation of operating cash flows. The findings also indicated that integrating financial data, managerial behavioral signals, decision-making patterns, and governance indicators can enhance the detection accuracy and practical usefulness of intelligent models. The proposed model suggests that intelligent detection of earnings management is effective when it moves beyond traditional financial statement analysis and incorporates behavioral, governance, and explainability dimensions. The model can support auditors, regulators, audit committees, and financial analysts in the early identification of earnings manipulation risks and in improving financial reporting quality.
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