Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks

• URL: http://arxiv.org/abs/2511.12122v1
• Date: Sat, 15 Nov 2025 09:18:32 GMT
• Title: Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks
• Authors: Yi Wang, Ruoyi Fang, Anzhuo Xie, Hanrui Feng, Jianlin Lai,
• Abstract summary: This study addresses the problem of dynamic anomaly detection in accounting transactions.<n>It proposes a real-time detection method based on a Transformer to tackle the challenges of hidden abnormal behaviors.
• Score: 1.869032078105649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study addresses the problem of dynamic anomaly detection in accounting transactions and proposes a real-time detection method based on a Transformer to tackle the challenges of hidden abnormal behaviors and high timeliness requirements in complex trading environments. The approach first models accounting transaction data by representing multi-dimensional records as time-series matrices and uses embedding layers and positional encoding to achieve low-dimensional mapping of inputs. A sequence modeling structure with multi-head self-attention is then constructed to capture global dependencies and aggregate features from multiple perspectives, thereby enhancing the ability to detect abnormal patterns. The network further integrates feed-forward layers and regularization strategies to achieve deep feature representation and accurate anomaly probability estimation. To validate the effectiveness of the method, extensive experiments were conducted on a public dataset, including comparative analysis, hyperparameter sensitivity tests, environmental sensitivity tests, and data sensitivity tests. Results show that the proposed method outperforms baseline models in AUC, F1-Score, Precision, and Recall, and maintains stable performance under different environmental conditions and data perturbations. These findings confirm the applicability and advantages of the Transformer-based framework for dynamic anomaly detection in accounting transactions and provide methodological support for intelligent financial risk control and auditing.

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