  Bank credit risk is a significant challenge in modern financial transactions,
and the ability to identify qualified credit card holders among a large number
of applicants is crucial for the profitability of a bank'sbank's credit card
business. In the past, screening applicants'applicants' conditions often
required a significant amount of manual labor, which was time-consuming and
labor-intensive. Although the accuracy and reliability of previously used ML
models have been continuously improving, the pursuit of more reliable and
powerful AI intelligent models is undoubtedly the unremitting pursuit by major
banks in the financial industry. In this study, we used a dataset of over
40,000 records provided by a commercial bank as the research object. We
compared various dimensionality reduction techniques such as PCA and T-SNE for
preprocessing high-dimensional datasets and performed in-depth adaptation and
tuning of distributed models such as LightGBM and XGBoost, as well as deep
models like Tabnet. After a series of research and processing, we obtained
excellent research results by combining SMOTEENN with these techniques. The
experiments demonstrated that LightGBM combined with PCA and SMOTEENN
techniques can assist banks in accurately predicting potential high-quality
customers, showing relatively outstanding performance compared to other models.
