The Path of Financial Shared Service Model Based on Deep Neural Network Algorithm

With the increasing trend of globalization, large and medium-sized enterprise groups have been waiting for the opportunity to set up branches and subsidiaries to devote themselves to scale development. However, the nearly identical financial operations of each branch are not properly integrated, requiring the allocation of a large number of accounting staff, which leads to the increasing problems of rising corporate costs and the execution of financial strategies not matching expectations. In the process of combining big data, cloud computing and other information technologies with deep neural network(DNN), the financial shared service model(FSSM) has emerged, and this model has largely contributed to the upgrade and change of the traditional accounting industry. The FSSM has not only centralized the accounting work of enterprises, but also the related risks. This paper analyzes the human resource and cost effects of the implementation of the FSSM of Group S. By reflecting the business situation of Group S more quickly, accurately and timely, we find that the implementation of the FSSM can save labor costs and improve financial management. In addition, this paper analyzes the application of DNN on financial forecasting and crisis warning, and proposes an optimization path of FSSM based on DNN algorithm to support the business development of Group S more efficiently.