A Comparative Study of Financial Big Data Standard System Based on Deep Learning Algorithms

Abstract

The standard system of financial big data involves a wide range of contents and diversification. Financial institutions in the process of operation and social sectors constitute a huge interweaving network, precipitating a large number of data. In this context, data security is particularly important. Therefore, based on the deep learning algorithm, the author compares and studies the financial big data standard system. The in-depth learning model is introduced into the financial market and combined with the traditional statistical model to forecast the volatility of the financial market and calculate its risk value. Through the research and comparative analysis of the domestic and international financial big data standard norm system, it is found that part of the domestic financial big data standard specification is revised by reference, while the other part has the characteristics of Chinese financial market. However, there is still room for further development in terms of financial big data regulation, information security, financial enterprise big data platform construction and analytical capabilities.