Enhancing Human Resource Management through Deep Learning: A Predictive Analytics Approach to Employee Retention Success

This research explores the integration of deep learning in Human Resource Management (HRM) to increase the accuracy of predictive modeling in successful hiring and retention. It should be noted that HR data is very complex and so most traditional analytical methods usually fall short of providing accurate forecasts that are integral for management-level decision-making. This research introduces two deep learning models: a deep neural network (DNN) for an employee turnover prediction together with a CNN for NLP use cases for recruitment success assessment. A model's performance is assessed through accuracy, area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE) metrics which are carried out by the study using the datasets that comprise various HR metrics. The study reveals that the DNN model reaches 92% and 0.94 respectively in the accuracy and AUC parameters for employee retention predictions, whereas the convolutional neural network (CNN) model gives an accuracy of 95% and 0.97 in proving recruitment success. These outperform the accuracy of various predictive analytics techniques, demonstrating DLLM's capability to transform HRM practices by using high-precision analytics in managing human capital. This study adds to the literature by demonstrating beyond a doubt deep learning as a utility and a fact in HRM, but at the same time, offering practical insights that can be harnessed by HR professionals to use the same for high-yield data-driven HM decisions. Moreover, this area covers the ethical issues and inherent biases that are involved in using AI in HR. Implementing responsible AI in this domain is strongly recommended. The present study has great implications for the whole debate about the integration of AI into HR strategies. The study shows how HR procedures can be greatly enhanced by the article. The future research agenda will involve establishing the applicability of these AI models in various industries as well as investigating the associated long-term consequences of AI-powered HRM.