Under the trend of combining the banking industry with the Internet, bank customer classification based on data analysis is important for enterprises to conduct accurate marketing and thus increase revenue. Based on the current situation, this overview study takes banking customer classification as the theme, examining the relevant research in this field between 2013 and 2023. The characteristics of customer classification in this industry, the focuses of existing research, and the future direction of attention are summarized. Due to the large volume and relative multidimensionality of customer data involved in the banking industry, most of the classification models are built on the basis of the standard model, which is improved to make the operation more efficient and accelerate the speed of convergence. On this basis, this paper proposes that bank customer classification should be improved in the algorithmic model while pay more attention to its operating effect based on real-world scenarios. Meanwhile, feature engineering, which plays an important role in data mining, is attracting attention, and more research may be carried out in this direction in the future. In addition, the research on customer segmentation dynamics is important but seldom addressed, which is an area for deeper cultivation.