An Evaluation Method of Company Payment Status Based on Historical Data of Provident Fund Payment

The housing provident fund system is the state's incentive policy to solve the housing problems of low-income families, and more and more companies are beginning to pay provident funds. In the past, there is little research on the evaluation method of the company provident fund payment status. To effectively evaluate the willingness and ability of the company to pay the provident fund, this paper designs an evaluation method of the company's payment status based on historical data of the provident fund. This method can be used as a basis for future provident fund evaluations. First, construct a feature set from multi-source data, and use Spearman correlation coefficient to extract the optimal feature subset. Then, according to the two features that have the greatest impact on the provident fund, the provident fund payment rating is calculated. Finally, we select random samples as the training set, and train in models such as LightGBM Random Forest XGBoost DeepGBM. The results show that LightGBM can be better applied to the evaluation of historical provident fund data.