AI can predict job turnover from resumes with 98% accuracy
Researchers trained a deep learning model on 90,000 resumes to forecast how long employees will stay in their jobs, achieving near-perfect predictive accuracy. The breakthrough could help companies cut costly turnover and hire smarter—but raises questions about fairness when algorithms screen candidates based on career patterns.
Originaltitel: Optimizing talent supply chains: deep learning models for resume-based retention forecasting
Abstract In today’s dynamic labor market, organizations face growing challenges in attracting and retaining top talent. High employee turnover imposes substantial costs and disrupts strategic workforce planning. The aim of this paper is to investigate where predictive modeling, specifically deep learning, can be applied to improve recruitment strategies by forecasting candidate retention time, based only on resume data. Conducted in collaboration with Talendary, an AI-driven recruitment platform, the study presents a neural network regression model trained on over 90,000 anonymized resumes. Utilizing principles from Supply Chain Management, the model incorporates features such as career progression rate, job stability, and mobility patterns. The results demonstrate strong predictive performance, achieving an $$R^2$$ score of 0.9877 on the test set, highlighting that resumes carry meaningful signals about future job tenure. These findings suggest that data-driven recruitment, supported by machine learning, can enable more informed hiring decisions, reduce turnover-related costs, and contribute to long-term workforce sustainability. Ethical considerations, including fairness, transparency, and privacy, are also addressed to support responsible AI use in hiring processes.