Construction and Application of a Financial Big Data Analysis Model Based on Machine Learning

Abstract

This paper attempts to improve the real-timeliness and value of financial services, and realize scientific decision-making in financial processes. For these purposes, the application mechanism of big data technology in the financial field was discussed in details, and the procedure of financial data preprocessing was explained. Based on user portrait of financial behaviors and machine learning (ML), the authors established a financial big data analysis (BDA) model, in which weight coefficients are assigned reasonably to three networks, namely, Bayesian network (BN), backpropagation neural network (BPNN), and autoregressive integrated moving average (ARIMA) network. In this way, the proposed model combines the merits of all three networks. Experimental results show that the proposed user portrait greatly facilitates the construction of causality in a large sample set, and the prediction curve of the proposed combinatory prediction model achieves the smallest prediction error.

Analysis of implementation for big data techniques in consumer behavior

“…Therefore, big data technology can still be important to every step of the AISAS model, as shown in Figure 1. Even in searching and sharing steps which are different from the AIDMA model, big data technology can still be helpful to businesses, thanks to its powerful ability to collect, process, and summarize data, it allows businesses to keep up with the trend of the times while capturing the common and individual characteristics of consumers [12].…”