Optimizing Human Resource Management in Financial Institutions based on XGBoost for Workforce Planning

Human Resource Management (HRM) is an essential function within financial institutions with a direct bearing on workforce productivity, employee morale, and business success as an organization. Classic HRM is predominantly based on manual intervention and rule-based decisions, which contribute to inefficiency, discriminatory recruitment, and limitations in forecasting worker performance and turnover. Presently available machine learning-based HRM systems try to solve these challenges but face scaling issues, difficulty in interpretability, and volatility in prediction efficiency, which in turn restrict their usability in practical financial institutions. To address these difficulties, this study presents a new HRM system with the help of the eXtreme Gradient Boosting (XGBoost) algorithm. XGBoost is renowned for its high accuracy, efficiency in computation, and successful processing of large datasets. The suggested model uses past HR information to examine the performance of workers, forecast attrition patterns, and maximize recruitment and workforce planning. An extensive dataset including worker records, performance metrics, and past attrition was employed for training and validating the model. Experimental outcomes show that the XGBoost-based HRM system performs better than conventional machine learning models, with better prediction accuracy and retention of interpretability. These results highlight the prospects of incorporating machine learning into HRM to optimize workforce management, reduce employee turnover, and facilitate data-driven decision-making in banks.