An Improved Attention-Based LSTM Neural Network for Intelligent Anomaly Detection in Financial Statements

Fraudulent financial reporting continues to undermine market integrity and threaten stakeholder interests globally. Conventional analytical methodologies demonstrate limited effectiveness in recognizing intricate temporal relationships and nuanced irregularities characteristic of accounting manipulation. This research introduces an enhanced recurrent neural architecture incorporating multi-head attention layers with bidirectional LSTM components, specifically engineered for identifying accounting anomalies. Our architectural design combines attention-weighted feature selection with dual-directional temporal processing to simultaneously extract prolonged sequential patterns and pivotal indicators spanning multiple reporting cycles. Through rigorous evaluation using a dataset of 2,500 corporate entities across 12 reporting cycles, our methodology achieves 93.87% classification accuracy, 92.41% positive predictive value, 91.56% sensitivity, and 91.98% harmonic mean of precision-recall. These metrics surpass conventional statistical methods and standard deep architectures by substantial margins. The attention weight distributions offer transparent interpretability, revealing which specific accounting ratios and temporal windows contribute decisively to fraud predictions, thereby enhancing practical deployment feasibility in professional audit environments.