Wu Peifeng, Chen Yaqiang
School of Statistics, Jilin University of Finance and Economics, Changchun, Jilin, China.
PeerJ Comput Sci. 2024 Nov 26;10:e2532. doi: 10.7717/peerj-cs.2532. eCollection 2024.
The detection of corporate accounting fraud is a critical challenge in the financial industry, where traditional models such as neural networks, logistic regression, and support vector machines often fall short in achieving high accuracy due to the complex and evolving nature of fraudulent activities. This paper proposes an enhanced approach to fraud detection by integrating convolutional neural networks (CNN) and long short-term memory (LSTM) networks, complemented by an attention mechanism to prioritize relevant features. To further improve the model's performance, the sparrow search algorithm (SSA) is employed for parameter optimization, ensuring the best configuration of the CNN-LSTM-Attention framework. Experimental results demonstrate that the proposed model outperforms conventional methods across various evaluation metrics, offering superior accuracy and robustness in recognizing fraudulent patterns in corporate accounting data.
企业会计欺诈的检测是金融行业面临的一项关键挑战，在该行业中，诸如神经网络、逻辑回归和支持向量机等传统模型，由于欺诈活动的复杂性和不断演变的特性，往往难以实现高精度。本文提出了一种通过整合卷积神经网络（CNN）和长短期记忆（LSTM）网络来增强欺诈检测的方法，并辅以注意力机制来对相关特征进行优先级排序。为了进一步提高模型性能，采用麻雀搜索算法（SSA）进行参数优化，确保CNN-LSTM-注意力框架的最佳配置。实验结果表明，所提出的模型在各种评估指标上均优于传统方法，在识别企业会计数据中的欺诈模式方面具有更高的准确性和鲁棒性。
