Department of Engineering Management, Anhui Audit College, Hefei 230601, China.
School of Management, Hefei University of Technology, Hefei 230009, China.
Comput Intell Neurosci. 2022 Aug 28;2022:8282854. doi: 10.1155/2022/8282854. eCollection 2022.
The entire auditing process is complicated and tedious and requires a lot of human resources. Therefore, the intelligent development of auditing is the general trend. In order to improve the audit quality, this paper establishes an intelligent financial audit model that can predict the audit opinion of the consolidated financial statements. This paper proposes an audit opinion prediction model based on the fusion of deep belief neural network (DBN) and long-short term memory (LSTM). First, an indicator system is established for audit opinions, and multiple financial parameters are used to describe possible audit opinions. On this basis, a DBN network is designed to complete deep feature extraction and used for LSTM training. According to the prediction model obtained by training, the subsequent audit opinion can be scientifically predicted. In the experiment, the method in this paper is tested based on financial audit related data sets and compared with the prediction results of traditional multilayer perceptron (MLP), convolutional neural network (CNN), and LSTM models. The results verify the validity and reliability of the model in this paper.
整个审计过程复杂繁琐，需要大量的人力资源。因此，审计的智能化发展是大势所趋。为了提高审计质量，本文建立了一个能够预测合并财务报表审计意见的智能财务审计模型。本文提出了一种基于深度置信网络（DBN）和长短时记忆（LSTM）融合的审计意见预测模型。首先，建立了审计意见指标体系，使用多个财务参数来描述可能的审计意见。在此基础上，设计了一个 DBN 网络来完成深度特征提取，并用于 LSTM 训练。根据训练得到的预测模型，可以对后续的审计意见进行科学预测。在实验中，基于财务审计相关数据集对本文方法进行了测试，并与传统多层感知机（MLP）、卷积神经网络（CNN）和 LSTM 模型的预测结果进行了比较。结果验证了本文模型的有效性和可靠性。
