A Review On Machine Learning Driven Frameworks For Employee Behaviour Prediction And Proactive Organizational Risk Management In Human Resource Management

Authors: Abhendra Pratap Singh, Nehal Sharma, Kanika, Nandini Sharma

Abstract: In today’s era, many multinational companies (MNCs) operate worldwide. In these organizations, human resource management and senior managers face several challenges, such as employees leaving their jobs suddenly, low performance, stress and burnout, and skill gaps. This paper reviews the use of machine learning, artificial intelligence (AI), and sentiment analysis to address these problems effectively. These tools help predict which employees may leave their jobs, who may underperform, potential future workforce risks, and other related issues. The system works by collecting employee data, cleaning and preparing the data, predicting risks, and assisting human resource management in taking early action. This approach provides several benefits, such as reducing employee turnover, saving company costs, and improving employee satisfaction. The paper reviews machine learning based frameworks that identifies human resource problems at an early stage to avoid such issues. It aims to transform human resource management from a reactive approach to a proactive one and improve decision-making for betterment.

DOI: http://doi.org/10.5281/zenodo.21769373