Strategic Human Resource Management Innovations in Finance and Marketing with ML

Due to manual procedures, static data analysis, and reactive strategies, traditional Strategic Human Resource Management (SHRM) systems in marketing and finance suffer from inefficiencies and delayed decision-making. The proposed system incorporates automation, real-time data processing, and predictive analytics along with machine learning (ML) to overcome these drawbacks. Utilizing Random Forest (RF) for automated talent acquisition, Long Short-Term Memory (LSTM) networks for predictive workforce planning, and KNearest Neighbors (KNN) for individualized employee development are some of the key features. The results show notable progress: RF obtains an F1-score of 0.84, an LSTM of 0.77, and a KNN of 0.90. Additionally, the proposed system increases training efficacy by improving employee satisfaction from $\mathbf{7 0 \%}$ to $\mathbf{8 5 \%}$ and skill improvement from $\mathbf{6 0 \%}$ to $\mathbf{7 5 \%}$. Employee retention rates increase from $\mathbf{7 5 \%}$ in 2020 to $\mathbf{8 7 \%}$ in 2023. By transforming HR operations from reactive to proactive, the ML-integrated SHRM system improves corporate performance and decision-making.