Employee Churn Prediction Platform: A Cloud-Native HR Analytics System with Generative AI-Driven Retention Strategies and Exit Interview Simulation

Abstract

Employee attrition continues to be a critical issue for organizations, leading to increased recruitment costs, loss of skilled talent, and disruptions in workflow efficiency. Addressing this challenge requires not only accurate prediction but also actionable insights that support timely intervention. This paper introduces the Employee Churn Prediction Platform (ECPP), a cloud-based intelligent system designed to identify employees at risk of leaving and assist human resource teams in making informed retention decisions.The proposed platform leverages a serverless architecture built on AWS services, where employee-related data is collected, stored, and processed efficiently. Machine learning models, particularly XGBoost, are utilized to analyze historical patterns and predict the likelihood of employee churn with high accuracy. To enhance interpretability and usability, the system integrates a generative AI-based HR Retention Advisor that converts prediction results into meaningful recommendations tailored to individual employee profiles. A unique feature of this platform is the Exit Interview Simulation module, which creates virtual employee scenarios based on predicted risk factors. This allows HR professionals to simulate conversations and evaluate potential retention strategies before applying them in real-world situations. Experimental evaluation demonstrates strong predictive performance, achieving an F1-score of 0.76 and an AUC-ROC of 0.91, along with positive feedback from users regarding system usability and effectiveness.Overall, the ECPP provides a scalable and practical solution for modern workforce management by combining predictive analytics, cloud computing, and AI-driven decision support.

Introduction

This paper presents the Employee Churn Prediction Platform (ECPP), a cloud-based system that uses machine learning (ML) and generative AI (GenAI) to predict employee attrition and support proactive retention strategies. Employee turnover is costly, yet many organizations rely on reactive methods such as exit interviews after employees decide to leave. ECPP addresses this challenge by identifying employees at high risk of resigning before they submit notice, enabling HR teams to intervene early.

The platform is built on Amazon Web Services (AWS) and includes an end-to-end pipeline for data ingestion, preprocessing, model training, real-time prediction, and AI-generated retention recommendations. Employee data—including demographics, job history, performance, compensation, and engagement scores—is collected through AWS services, processed using AWS Glue, and analyzed using machine learning models deployed in Amazon SageMaker.

The system evaluates three ML algorithms: Logistic Regression, XGBoost, and Neural Networks. To address the class imbalance common in attrition datasets, it uses SMOTE and cost-sensitive learning. Among the models, XGBoost achieved the best performance with an 88.7% accuracy, 0.76 F1-score, and 0.91 AUC-ROC. Model predictions are explained using SHAP (Shapley Additive Explanations), which identifies the key factors influencing each employee's churn risk, improving transparency and trust.

A key innovation is the Generative AI HR Retention Advisor, which converts prediction results into personalized retention reports. Based on churn probability, SHAP explanations, and employee information, the AI generates easy-to-understand risk summaries, recommended retention actions, and conversation starters that managers can use during discussions with employees.

Another novel contribution is the Exit Interview Simulation module, which creates AI-generated employee personas for HR professionals to practice retention conversations before meeting actual employees. A user study involving HR professionals found the simulation to be realistic, coherent, and practically useful for preparing effective retention strategies.

Experimental results identified the strongest predictors of employee attrition, including overtime work, low salary, short tenure with the current manager, low job satisfaction, poor work-life balance, and delayed promotions. Compared with traditional HR systems and ML-only approaches, ECPP provides superior predictive accuracy while also offering explainable insights and AI-driven decision support.

The paper also discusses ethical considerations, emphasizing that the system supports—not replaces—human decision-making. It incorporates role-based access control, fairness audits, privacy protections, and compliance with data protection regulations.

Finally, the authors acknowledge limitations such as reliance on periodic data updates, limited organizational knowledge in AI recommendations, and a small-scale user study. Future work includes integrating real-time data streams, Retrieval-Augmented Generation (RAG) for organization-specific advice, larger validation studies, and improved models for departments with limited historical data.

Conclusion

This paper has presented the Employee Churn Prediction Platform, a cloud-native HR analytics system that combines serverless data ingestion via Amazon API Gateway and AWS Lambda, scalable preprocessing with AWS Glue, machine learning model management through Amazon SageMaker, and a generative AI HR Retention Advisor to help organizations proactively address employee attrition. The XGBoost model achieved an F1-score of 0.76 and an AUC-ROC of 0.91, and SHAP-based explainability ensures that every prediction is accompanied by transparent, feature-level attribution accessible to non-technical HR professionals. The Exit Interview Simulation module represents a novel contribution to the HR analytics literature and to the broader field of human-AI collaboration in workforce management. By enabling practitioners to rehearse retention conversations with AI-powered virtual personas before real employee interactions, the platform reduces the risk of poorly timed or poorly framed retention overtures and builds practitioner confidence in handling sensitive employment conversations. User study results confirm high satisfaction with persona believability, conversation coherence, and debrief utility. The ECPP demonstrates that modern cloud infrastructure, interpretable machine learning, and generative AI can be meaningfully integrated into a cohesive, ethically grounded HR analytics platform that is both technically rigorous and practically deployable in real organizational settings. Organizations adopting this platform can expect measurable improvements in the timeliness and precision of their retention efforts, translating to reduced attrition costs and stronger workforce stability over time.

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Copyright

Copyright © 2026 Selvi V, Akshya A, Rohan Kumar K V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.