AI in Turnover Risk Assessment: Early Warning Algorithms and Employee Retention Strategies

This paper reviews artificial intelligence approaches to predicting the risks of employee turnover and managing strategies designed to retain them. The purpose of the current study is to carry out a systematic review and practical assessment of existing algorithms used as early warnings for personnel turnover in corporate environments and to recommend ways through which the derived models could be incorporated into HR management processes. The relevance of this work is determined by organizations’ enormous costs associated with replacing specialists, the rapid growth of the HR analytics market, and the need to shift from a reactive turnover management model to a proactive talent-retention system. The novelty of the research lies in the comprehensive comparison of classical statistical methods (logistic regression, CoxRF) and modern machine learning algorithms (XGBoost, LSTM-RNN, Bidirectional-TCN, graph neural networks) on both proprietary and open datasets, as well as in the incorporation of interpretability criteria (SHAP, LIME), organizational and ethical barriers, MLOps requirements, and EU regulatory standards into the architecture of predictive HR systems. The findings demonstrate that basic statistical models provide a rapid start and clear interpretability on small samples; however, as data volumes grow, gradient boosting emerges as the “gold standard,” and recurrent and convolutional networks become preferable for analyzing temporal communications. Graph neural networks improve flight-risk detection quality by accounting for social connections, while interpretability tools enable the translation of a score into a concrete retention plan. The key takeaway is the need for an integrated approach: starting from detailed data prep and cleanup, building a cross-functional team, setting up an MLOps loop, designing solutions ethically, training end-users, and monitoring success metrics regularly. This paper will be helpful to HR directors, people analytics specialists, AI-in-HR project managers, as well as academic researchers in the field of human capital management.