Financial Risk Control Model Based on Deep Neural Networks

Abstract

Under the background of global economic integration, commercial banks are facing more and more complex business environment. As one of the major financial risks faced by commercial banks, liquidity risk determines and reflects the safety and profitability of bank operation. Based on joint-stock commercial banks as the research object, this paper, respectively, from the angle of the static and dynamic measurements and projections for liquidity risk and based on the current situation of four joint-stock commercial banks liquidity level study, tries to explore the change law of commercial banks liquidity risk and financial risk control of commercial bank and puts forward reasonable suggestions. In this paper, an AHP neural network model combining subjective and objective methods is proposed. This method can not only overcome the defects of the single evaluation method but also improve the data accessibility by using the qualitative data and quantitative data of AHP. In the aspect of financial risk control system, this paper tries to establish a more comprehensive and practical financial risk control model by combining the previous research of scholars, the business model process, and the experience of practical workers.

Enterprise accounting services applying big data under the financial sharing model

“…The continuous expansion of the enterprise scale, the demand for talent is also increasing year by year, to the accounting work of the enterprise accounting has also brought great challenges [13][14]. The complexity of enterprise financial affairs, the workload is doubled, not only affects the efficiency of enterprise accounting, but also brings certain side effects to the development of enterprises [15][16][17]. The traditional manual bookkeeping, accounting, reporting, and other work methods have not been able to solve the heavy workload of enterprise accountants [18][19][20].…”