An Intelligent AI-Driven Decision-Making Framework Integrating Human Resource Analytics, Financial Intelligence, Predictive Modelling, and Strategic Organizational Management

Abstract

Artificial Intelligence (AI) may bring about a breakthrough in the field of HRM providing better workforce analytics, better employee retention and helping with strategic decisions. In this paper, we present an explainable AI decision making system including HR Analytics, compensation intelligence and predictive modeling. 1470 employee records were analyzed for demographic factors, compensation and organizational attributes. Three machine learning algorithms were compared based on such parameters as accuracy, precision, recall, f1-score and ROC-AUC. It turned out that 16.12% of the employees have quit and there were significant differences in the variables of overtime work, organizational tenure and monthly income. The most accurate prediction was made by the Logistic Regression algorithm with ROC-AUC being 0.8115 and F1 score 43.84%. After conducting the SHAP analysis the predictors for our model were identified as stock option levels, overtime work, employee age and organizational characteristics. The framework that we propose includes some predictive results along with insights which were easy to understand and were useful in assessing workforce retention and planning. However, one should mention that this attrition detection algorithm had very poor sensitivity and further development was needed.