Attention Mechanisms for Identifying Earnings Manipulation Signals in Corporate Financial Statements

Abstract

The detection of earnings manipulation in corporate financial statements represents a critical challenge for investors, auditors, and regulatory bodies. Traditional statistical methods and rule-based systems often struggle to identify sophisticated manipulation schemes that leverage complex accounting techniques and temporal patterns. This paper proposes a novel approach that integrates attention mechanisms with deep learning architectures to enhance the detection of earnings manipulation signals. By leveraging the selective focus capabilities of attention layers, our framework automatically identifies the most relevant financial indicators and temporal dependencies that signal potential fraudulent activities. The methodology combines sequence-based neural networks with dual-stage attention mechanisms to analyze both cross-sectional financial ratios and longitudinal patterns in accounting data. Experimental results demonstrate that attention-enhanced models achieve superior performance compared to conventional fraud detection approaches, offering improved accuracy while maintaining interpretability through attention weight visualization. This research contributes to the growing intersection of artificial intelligence and forensic accounting, providing practitioners with advanced tools for financial statement analysis and fraud prevention.