Zhaoxin Li1, Yinkai Niu2, Xinghan Chen3, Cheng Huang4
1Communication University of China, Beijing, China
2Kunming University of Science and Technology, Kunming, Yunnan, China
3TEDA NO. 2 Middle School, Tianjin, China
4Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu, China
For the past few years, with the continuous progress of technology, the rapid integration of traditional financial industry and the Internet has given rise to online financial businesses that deal with high-concurrency, large-scale, and multidimensional data. However, due to the high profitability and high risk of the financial industry, as well as the upgrading of fraudulent means, this transformation has also put higher requirements on financial risk control. Recently, advanced technologies represented by big data and artificial intelligence have provided new directions for improving risk control capabilities for commercial banks, with machine learning playing an increasingly important role. This paper aims to predict customer fraud behavior by conducting data analysis, data preprocessing, feature engineering , dataset partitioning, and using XGBoost and LightGBM algorithms in machine learning, in order to provide assistance in ensuring the stable operation of financial institutions and the security of customer assets.
machine learning, digital finance, anti-fraud, ensemble learning, XGBoost
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