★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★★Open research. Global perspectives.
editor@theamericanjournals.comPublisher Home ↗
Management and Economics OPEN ACCESS

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

Zvezdilin Anatoly
PhD in Economics, Lomonosov Moscow State University (Russia). Member of the Association for Talent Development (USA) San Diego, California, USA
DOI Crossref DOI 10.37547/tajmei/Volume07Issue08-04
tajmei 2025
VOL. 7 / NO. 8 AUGUST
FULL TEXT PP. 38-45
DOWNLOAD PDF

View all articles in this issue ↗

VOLUME 7
ISSUE 8
YEAR 2025
PAGES 38-45
RESEARCH OVERVIEW

Abstract

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.

RESEARCH TOPICS

Keywords

artificial intelligence turnover prediction early warning algorithms employee retention strategies
SOURCES & SCHOLARSHIP

References

  1. Buyens, D., & Quataert, S. (2025, April 2). Lack of expertise, time, and budget are the biggest stumbling blocks to implementing AI in HR. Vlerick. https://www.vlerick.com/en/insights/lack-of-expertise-time-and-budget-are-the-biggest-stumbling-blocks-to-implementing-ai-in-hr/
  2. Ganapathisamy, S. (2023). A Long Short-Term Memory with Recurrent Neural Network and Brownian Motion Butterfly Optimization for Employee Attrition Prediction. International Journal of Intelligent Engineering and Systems, 17(1), 183–192. https://doi.org/10.22266/ijies2024.0229.18
  3. Global Growth Insights. (2025a). AI Recruitment Market Trends. Global Growth Insights. https://www.globalgrowthinsights.com/market-reports/ai-recruitment-market-116973
  4. Global Growth Insights. (2025b). Learning Experience Platform Market Size. Global Growth Insights. https://www.globalgrowthinsights.com/market-reports/learning-experience-platform-lxp-market-102110
  5. HR Future. (2024). HR.com’s Future of AI and Recruitment Technologies 2024-25: Carefully leverage AI to reap greater success in recruitment. HR Future. https://eightfold.ai/wp-content/uploads/hr_future_of_ai_and_recruitment_technologies_report-1.pdf
  6. Kapadia, S. (2025, March 12). AI is transforming employee onboarding, saving HR days. Business Insider. https://www.businessinsider.com/generative-ai-employee-onboarding-human-resources-2025-3
  7. Leidner, J. L. (2024, May 13). Challenges and Opportunities of NLP for HR Applications: A Discussion Paper. Arxiv. https://arxiv.org/pdf/2405.07766
  8. Ma, X., Liu, W., & Zhao, C. (2024). Can Large Language Models Predict Employee Attrition? ArXiv. https://doi.org/10.48550/arxiv.2411.01353
  9. PR Newswire. (2025, June). HR Analytics Market Size to Surpass USD 9.89 Billion CAGR of 14.9% during 2025-2031. PR Newswire. https://www.prnewswire.com/news-releases/hr-analytics-market-size-to-surpass-usd-9-89-billion-cagr-of-14-9-during-20252031--302492353.html
  10. Shiri, F. M., Yamaguchi, S., & Ahmadon, M. A. B. (2025). A Deep Learning Model Based on Bidirectional Temporal Convolutional Network (Bi-TCN) for Predicting Employee Attrition. Applied Sciences, 15(6), 2984. https://doi.org/10.3390/app15062984
  11. SHRM Labs. (2023, September). Employee Retention Technologies. SHRM Labs. https://www.shrm.org/content/dam/en/shrm/shrm-labs/documents/222395-SHRMLabs-_-TechStars-Report-TechnologiesRetention_R3-2.pdf
  12. Varkiani, S. M., Pattarin, F., Fabbri, T., & Fantoni, G. (2025). Predicting employee attrition and explaining its determinants. Expert Systems with Applications, 272(126575), 126575–126575. https://doi.org/10.1016/j.eswa.2025.126575
  13. Visier. (2024, June 4). Visier’s Research Finds 87% of People Managers Asking for AI Tools to Make Their Jobs Easier. Visier. https://www.visier.com/company/news/hr-generative-ai-for-people-managers-na/
  14. Zhu, Q., Shang, J., Cai, X., Jiang, L., Liu, F., & Qiang, B. (2019, August 1). CoxRF: Employee Turnover Prediction Based on Survival Analysis. IEEE Xplore. https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00212