The future of Human Resource Management: Predictive Insights through Machine Learning

• Arifa Siddiqua

  Author

• Md Anwarul Morshed

  Author

• Ferdousi Akter

  Author

• Fahima Rahman

  Author

The transition of Human Resource Management (HRM) from simple statistical approaches to sophisticated machine learning (ML) techniques signals a change in workforce management paradigms. To optimize HR operations such as hiring, retention, performance forecasting, and strategic workforce planning, this paper examines the revolutionary potential of predictive talent analytics enabled by machine learning. While contemporary machine learning techniques use extensive employee data, including demographics, performance histories, and engagement measures, to predict patterns in promotions, attrition, and productivity, traditional HR systems were only able to automate administrative duties without providing predictive insights. The paper critically analyzes current machine learning (ML) applications in HR, their effectiveness, difficulties, and ethical issues, including bias and privacy. It is based on theoretical frameworks such as human capital theory, Resource-Based View (RBV), and decision support systems. A thorough conceptual framework that identifies the main facilitators, obstacles, and effect evaluation standards for incorporating machine learning into human resource management is put forth. Superior predictive performance in talent analytics is demonstrated by empirical studies of models like logistic regression, decision trees, random forests, gradient boosting, and neural networks. In order to achieve a more adaptable, objective, and successful method of managing human capital in the digital age, this book offers practitioners and policymakers useful guidance for navigating the ethical, operational, and strategic complexities of data-driven HRM.

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