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

DOI

10.2991/978-94-6463-980-3_34How to use a DOI?

Keywords

Accounting flow; anomaly detection; self-attention mechanism; risk control

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.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yi Wang
AU  - Ruoyi Fang
AU  - Anzhuo Xie
AU  - Hanrui Feng
AU  - Jianlin Lai
PY  - 2025
DA  - 2025/12/26
TI  - Dynamic Anomaly Identification in Accounting Transactions via Multi-Head Self-Attention Networks
BT  - Proceedings of the 2025 5th International Conference on Business Administration and Data Science (BADS 2025)
PB  - Atlantis Press
SP  - 366
EP  - 378
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-980-3_34
DO  - 10.2991/978-94-6463-980-3_34
ID  - Wang2025
ER  -