Big Data Financial Algorithm Technology Based on Machine Learning Technology

Abstract

With the development and wide application of machine learning technology, the use of machine learning technology for economic algorithm technology research has become a new type of financial technology field. Today's financial big data has penetrated into all walks of life and has become an important factor of production. The extraction and application of massive amounts of data by humans heralds the arrival of a new wave of productivity growth and consumer surplus. Big data originally refers to a large number of data sets generated through batch processing or web search index analysis. This paper uses machine learning technology to explore and research big data financial algorithms, analyze risk control measures, report on the improvement and perfection of traditional finance, and analyze and study the future development of big data finance. The main research content of this paper is the analysis of big data financial algorithm technology by machine learning algorithms. Machine learning technology is one of the main methods to solve big data mining problems. Machine learning technology is a process of self-improvement using the system itself, so that computer programs can automatically improve performance through accumulated experience. This paper analyzes the relevant theories and characteristics of machine learning algorithms, and integrates them into the research of big data economic algorithm technology. The final result of the research shows that when the data volume is 1G, the training time of SVM is 8 minutes, while the training time of Bayesian is 12 minutes, and the data volume is relatively small. The SVM algorithm still has obvious advantages in training time.