Predictive HR Analytics for Employee Retention in the IT Sector: A Machine Learning Approach – A Systematic Review

Abstract

HR analytics has become one of the most important tools that help organisations to manage the challenges associated with the workforce in the age of digitalisation. Employee retention is more critical in the Information Technology (IT) sector, where talent shortages continue to beacome a problem coupled with high levels of attrition and an increasing cost of recruiting and training talent. Old fashioned descriptive approaches to HR though good in terms of historical reporting do not have the power to predict and obstruct turnover. This systematic review examines the usage of the machine learning (ML) in predictive HR analysis and focuses on its relevance in anticipating employee loss and developing proactive retention strategies. The review compiles results of the recent literature (20152025 ) on the various ML approaches to turnover behavior, namely, supervised learning, ensemble methods, and deep learning and points out their efficacy in modelling turnover behavior. Among the key findings of ML, one should pay attention to its ability to increase the accuracy of predictions, guide data-driven workforce planning, and provide concrete knowledge on employee engagement, performance, and satisfaction measurements. But there are still gaps in domain-specific data set, model interpretability, and ethics associated with HR decision-making. In future research, the consolidation of real-time analytics, IoT-based workforce observation, and hybrid ML-psychological functions should work to maintain accuracy and transparency. The paper highlights how predictive HR analytics have the potential of revolutionizing retention practices and maintaining competitive edge in IT industry.