Application of Data Mining Technology in Financial Data Analysis Methods Under the Background of Big Data

With the development of the times, the financial industry has gradually become the focus of people's attention. Financial data analysis can help banks analyze various information of customers and decide which business to engage in with them. However, the analytical capabilities of current financial data analysis methods are still insufficient, so new technologies need to be used to improve. The application of data mining technology in financial data analysis methods under the background of big data studied in this article aimed to improve the analytical ability of financial data analysis using data mining technology. This article tested the repayment rate of customers after using data mining technology through experiments. The data showed that the highest repayment rate was 92%, indicating that financial data analysis using data mining technology is very accurate in analyzing customers' repayment ability. The experimental test showed that the use of data mining technology can increase bank revenue by up to 19%, indicating that data mining technology can bring significant benefits to banks. Through these two experiments, it can be proven that data mining technology and financial data analysis methods have a high degree of compatibility.

I. Introduction

The financial industry often uses financial data analysis methods to analyze various aspects of customer data, which is beneficial for banks to evaluate customers and decide whether business can be processed for them. This is very helpful for banks to reduce unnecessary losses. How to optimize financial analysis methods is currently the focus of research, and improving the analytical ability of financial analysis methods can bring greater benefits to banks.