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

TL;DR

This paper introduces a Transformer-based real-time anomaly detection method for accounting transactions, effectively capturing global dependencies and outperforming baseline models in various evaluation metrics.

Contribution

It presents a novel application of multi-head self-attention networks for dynamic anomaly detection in complex trading environments, enhancing detection accuracy and robustness.

Findings

Outperforms baseline models in AUC, F1-Score, Precision, and Recall.

Maintains stable performance under different environmental and data perturbations.

Validates effectiveness through extensive experiments on a public dataset.

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.