Wuhan College, Wuhan 430000, China.
Comput Intell Neurosci. 2022 May 31;2022:4991244. doi: 10.1155/2022/4991244. eCollection 2022.
Traditional financial accounting has gradually evolved into management accounting in order to adapt to changing times and developments. To avoid being obliterated by the times, accountants must gradually improve their professional and comprehensive abilities in order to create greater value for businesses in the AI (Artificial Intelligence) era. This article presents an AI-based financial management optimization design and proposes an AI-based accounts receivable management optimization framework based on the existing information system. A typical financial distress early-warning model is built using the BPNN (BP Neural Network) model, and the training samples of listed companies' financial data are processed iteratively using the neural network algorithm to realize the visual modeling of the object-oriented neural network and learn the training samples. Finally, the network's ability to provide early warning is put to the test. The results show that BPNN's prediction accuracy is significantly higher than that of other types, especially after years of data, with prediction results exceeding 90%. The results show that the BPNN-based financial early-warning method is feasible.
传统财务会计为了适应时代的变化和发展，逐渐演变为管理会计。为了不被时代淘汰，会计人员必须逐步提高自己的专业和综合能力，以便在人工智能（AI）时代为企业创造更大的价值。本文提出了一种基于人工智能的财务管理优化设计，并基于现有的信息系统提出了一种基于人工智能的应收账款管理优化框架。使用 BPNN（BP 神经网络）模型构建了一个典型的财务困境预警模型，并使用神经网络算法对上市公司财务数据的训练样本进行迭代处理，实现面向对象神经网络的可视化建模和训练样本的学习。最后，对网络的预警能力进行测试。结果表明，BPNN 的预测准确率明显高于其他类型，特别是经过多年的数据后，预测结果超过 90%。结果表明，基于 BPNN 的财务预警方法是可行的。
