Financial Justification of Cloud-Based HRIS Using Random Forest for Cost-Benefit Prediction in Modern Organizations

Abstract

With the changing times in the area of organizational management, the use of Human Resource Information Systems (HRIS) with cloud technologies has become indispensable for improving operational efficiency and strategic decision-making. A predictive framework using the Random Forest machine learning algorithm is presented by this study to characterise the financial vindication of taking up cloud-based HRIS in current business. The system lays emphasis on the examination of the past organizational data and HR operational metrics for the decision of the pattern which influences the financial results of HRIS implementation. By deploying the ensemble learning potentials of Random Forest, the model guarantee robustness and precision in prediction as well as it handles complicated, non-linear relations in the data. The system structure assimilates a data preprocessing part for cleaning of input data, a feature selection mechanism to uncover critical contributors, and a predictive layer driven by Random Forest to project financial viability. Such an approach gives organizations a data-driven methodology to assess the return on investment and take informed decision regarding HRIS adoption.The proposed method's contributions exist in the way it can improve financial transparency, help strategic HR planning, and be a piece of support that reduce the uncertainties in technology investment decisions through prediction.