DYNAMIC DEPLOYMENT OF FINANCIAL PERSONNEL BASED ON THE HOLT-WINTERS ALGORITHM-PROPHET-LSTM PREDICTIVE MODEL

In the context of refined enterprise management, the dynamic optimal allocation of human resources has become the key to improving operational efficiency and reducing labor costs. Financial reimbursement, as a high-frequency and important business process, exhibits significant fluctuation in its workload due to influences from business cycles, project milestones, and other factors. A fixed number of finance personnel can easily lead to backlogs in expense review during peak periods, resulting in employee dissatisfaction, while creating idle human resources during off-peak periods. To resolve this contradiction, this paper aims to establish a dynamic adjustment mechanism for finance personnel based on predictive modeling. This study utilizes historical reimbursement claim volume time series data from enterprises, integrating the Prophet-LSTM predictive model based on the Holt-Winters algorithm to accurately predict future claim volumes for specific cycles (e.g., monthly, quarterly). Through this model, enterprise managers can proactively anticipate future workloads and dynamically adjust staffing levels for financial reimbursement positions accordingly, enabling flexible allocation of financial personnel. Empirical research demonstrates that this predictive model exhibits high accuracy, with an MSE of only 152.47, an RMSE as low as 12.35, an MAE of 7.89, a MAPE of just 6.58%, and an R2 as high as 0.948. The dynamic adjustment strategy based on this model effectively smooths workloads, significantly shortens reimbursement processing cycles, and enhances the overall utilization efficiency of corporate human resources. This study provides a feasible theoretical framework and practical pathway for enterprises to achieve data-driven human resource management in finance and other volatile business domains.