Abstract
Big data has brought a new round of information revolution. Faced with the goal of full coverage of audit and supervision, making full use of big data is the main method to promote the realization of the goal of full coverage of audit and supervision. Data analysis and utilization is an indispensable task of auditing. Actively exploring multidimensional and intelligent data analysis methods and developing big data audit cases are the new development direction of auditing. The convolutional neural network's excellent ability to extract data features well meets the relevant requirements of financial auditing. However, in practical applications, convolutional neural networks often encounter various problems such as disappearance of gradients and difficulty in convergence, which reduces its expected performance in financial audit applications. In order to make the performance of the financial audit model based on convolutional neural network more excellent, after summarizing the characteristics of genetic algorithm, this article applies genetic algorithm to the optimization of the convolutional neural network model. We applied genetic algorithm to optimize the initial weights of the convolutional neural network. The error sensitivity and learning rate changes of different hidden layers are discussed, the influence of different learning rates on the convergence speed of convolutional neural networks is analyzed, and the recognition performance of other algorithms on financial audit data sets is simulated and compared. We conducted experiments on the network structure and parameter optimization on the financial audit database. The results show that the recognition error rate of the convolutional neural network model with improved learning rate algorithm in the financial audit data set is lower than that of the multilayer feedforward network, so it has better performance.
Highlights
The development of the financial industry in the 21st century shows globalization and informatization, and is developing towards “cloud.” With the continuous deepening of the reform of the domestic financial industry, the mixed operation of the financial industry has become more and more obvious, which has led to changes in the financial auditing information environment [1, 2]
As many government financial institutions have taken the lead in implementing electronic and networked management, and the characteristics of complex and diversified data, government financial auditing should move towards the construction of cloud-based auditing in the same way as the development of the financial industry, so as to achieve cloud-based auditing and supervision [5]
Based on the actual problems encountered in the training of convolutional neural networks, this paper applies genetic algorithms to the optimization of deep convolutional neural network models to enhance model stability, increase model convergence speed, and improve expected results
Summary
The development of the financial industry in the 21st century shows globalization and informatization, and is developing towards “cloud.” With the continuous deepening of the reform of the domestic financial industry, the mixed operation of the financial industry has become more and more obvious, which has led to changes in the financial auditing information environment [1, 2]. When “cloud computing” is widely integrated into our modern information life, if auditors can design and innovate audit methods and technologies from the perspective of “cloud computing,” they can deal with the intricate financial “cloud” environment and establish a sound financial supervision system [3]. Computational Intelligence and Neuroscience level, the use of big data ideas to design new audit methods is of great significance for achieving audit goals and creating a new model of auditing “clouds” [6]. Is article briefly describes the concept of data mining and exemplifies the methods that may be used in financial audit data mining. E stochastic gradient descent algorithm of the improved learning rate algorithm is used to train parameters on the MNISI data set, and the influence of the relevant parameters of the model on the recognition rate is analyzed through experiments, and the error rate of other algorithms is compared to verify the feasibility of the algorithm We designed three sets of parameter models for comparative analysis and selected a model with a weight attenuation coefficient of 5. e stochastic gradient descent algorithm of the improved learning rate algorithm is used to train parameters on the MNISI data set, and the influence of the relevant parameters of the model on the recognition rate is analyzed through experiments, and the error rate of other algorithms is compared to verify the feasibility of the algorithm
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
