Financial Data Collection Based on Big Data Intelligent Processing
With the rapid growth of the Internet, almost all human and commercial behaviors can be digitized, resulting in the accumulation and deposit of a large amount of personal and business process data, which also include financial data. The financial industry has always been the climax of the development of the world industry, playing a huge role in the growth of the world economy. The application of financial big data has become a hot trend in the industry. It has been widely used in specific businesses involving banking, securities, insurance, payment, and clearing, and Internet finance in many fields, such as transaction fraud identification, consumer credit, credit risk assessment, stock market prediction, stock price prediction, and intellectual property risk pricing. For example, the international financial market can gather a large amount of idle funds on an international scale, thus meeting the needs of international economic and trade development. In recent years, advanced information technology has developed at high speed. Emerging technologies such as the Internet, big data cloud computing, and artificial intelligence have been more widely used in the financial industry. Financial data processing is particularly important, in this context, because data can truly reflect the attributes of objective things. Finance is an important part of the national economy and has a close relationship with all walks of life. Financial data are an objective description of the operation of financial enterprises and can also reflect the operation of the national economy. Only through financial data can the characteristics, laws, and operating conditions of financial activities be understood. Financial data analysis can help financial enterprises make economic decisions and play an essential role in improving economic efficiency.
The scale of financial data is growing due to the rapid growth of the financial industry. Increasingly, more kinds of data are being generated, and the real-time performance is stronger. However, it has become more difficult to obtain data, which has also attracted the attention of researchers in related fields. Several studies have focused on this issue. For example, Liang, Das & Kostyuk (2018) proposed a framework for understanding the national monitoring infrastructure by discussing how various government agencies cooperate to establish such a centralized data infrastructure to score credit and examining the different but interrelated processes of data collection, aggregation, and analysis. Müller, Fay & Brocke (2018) used a unique panel dataset that contained details of big data and analytics (BDA) solutions that 814 companies had between 2008 and 2014 and found that real-time BDA assets were related to an average increase of 3%–7% in enterprise productivity. The research results provided strong empirical evidence for BDA’s commercial value and highlighted essential boundary conditions. Goertzen (2017) discussed the key features of quantitative data and various research questions they could answer. Then, Goertzen listed various performance measurement standards and indicators that could be used in the information management environment to support the conclusions and provide evidence for the development decisions of e-book collections. Goertzen also provided a research framework that could be used to plan and define the set analysis project. By focusing on potential processes and strategic activities, Chanias, Myers & Hess (2019) showed that digital strategy formulation not only represented a breakthrough in the previous practice of strategic information system planning, but also revealed the new extreme of emergency strategy formulation. Chanias et al. also concluded that a digital transformation strategy was sustainable and had no predictable outcome. Many researchers have provided theoretical methods for financial data collection and analysis and have made certain achievements. However, with the development of a more intelligent life, financial data collection and analysis requirements have become more precise.
Financial applications are becoming increasingly common with the continued popularization of big data. Big data processing and analysis have greatly contributed to financial data collection and analysis. Lee (2017) presented an integrated view of big data by tracing the evolution of big data over the past 20 years. Lee discussed the data analytics necessary to handle various structured and unstructured data and merchant review data for data analysis applications and evaluated the influence of big data on key operational performance. Choi, Wallace & Wang (2018) discussed the existing big data-related analysis technologies and identified their advantages, disadvantages, and main functions. They discussed various big data analysis strategies to overcome their respective computing and data challenges, reviewed the literature, and revealed how different types of big data methods (i.e., technology, strategy, and architecture) were applied to different subject areas. Finally, Choi et al. investigated the practical use of big data analysis in top brand enterprises through case studies. These studies have certain reference values, but most focus on the theoretical level, not combined with their actual application status for analysis.
With the advent of the big data era in recent years, more and more financial industries are engaged in big data application practices. Big data are used to mine, collect, classify, integrate, process financial data, and obtain reliable information from them to analyze big data, discover economic operation rules, and formulate business strategies. Big data are a breakthrough technology with huge potential. In this context, using big data to collect and analyze financial data is the choice of most enterprises. Therefore, in this paper the authors combined financial data collection and analysis based on big data intelligence to study its effects on related fields in the financial industry. This study is a simulation experiment of financial data collection and analysis based on a Python crawler and k-means algorithm. Through the crawler financial Web site data, combined with the data visualization library in Python, the trend of stock turnover, price trend, and trend price and turnover can be expressed through the line, scatter, and other intuitive charts. The above data show this method has strong timeliness, which can be accurate to the data of every hour and minute. K-means, a Python-based crawler and clustering algorithm, has a powerful processing function for financial data. Hence, this case can be used as a reference for enterprises to conduct financial analysis and can be judged according to the extracted valuable data for data analysis.