Modeling of Financial Risk Control Imbalance Dataset Based on Benchmarking Management Optimization Algorithm

Imbalance modeling of financial risk control refers to a situation where the sample in the data set is unbalanced due to various factors, such as the credit level and repayment ability of different financial customers. In order to evaluate and control risks more accurately, it is necessary to model unbalanced data. This article mainly designs models for unbalanced data sets of financial risk control, and uses different algorithms to compare and analyze the computational capabilities of the algorithms. It studies the financial risk control of benchmarking management optimization algorithms. Experimental data show that the maximum AUC value and accuracy value obtained by the benchmarking management optimization algorithm in the risk control of different financial enterprises exceed 0.85. When creating a model, it is necessary to consider the characteristics of unbalanced data and apply appropriate algorithms and techniques to ensure the reliability and stability of the model. At the same time, it is necessary to continuously optimize and improve the model to adapt to different risk scenarios and customer needs.