International Journal of Science, Engineering and TechnologyAuthors: Wei Zhang, Jaehoon Park, Minseo Kim, Robert Anderson
Abstract: Ensuring pay equity, internal parity, and reward fairness has become a critical priority for organizations seeking to build transparent, compliant, and performance-driven compensation strategies. Modern enterprises manage complex compensation structures influenced by job architecture frameworks, market benchmarks, performance outcomes, and organizational hierarchies, yet identifying inequities within these structures remains a significant analytical challenge. Platforms such as SAP SuccessFactors capture extensive compensation and workforce data across modules, including base salary, variable pay, job family classifications, grade structures, and performance ratings. However, traditional compensation analysis methods often rely on static reports and rule-based comparisons that fail to detect subtle disparities arising from multidimensional factors such as role alignment, experience levels, and internal job relationships. This study proposes an AI-powered analytical framework for evaluating pay equity, internal parity, and reward fairness using compensation and job architecture data integrated within SAP HANA Cloud. The framework applies machine learning techniques to model compensation patterns across job families, grades, and employee cohorts, enabling the identification of pay inconsistencies and structural imbalances within organizational compensation systems. By incorporating features such as job level alignment, performance ratings, tenure, and skill attributes, the model evaluates compensation fairness across comparable employee groups and detects deviations from expected pay distributions. The proposed approach supports key use cases including pay gap analysis, internal benchmarking, fairness scoring, and bias detection, providing organizations with actionable insights to improve compensation governance. The findings demonstrate that AI-driven compensation analytics can enhance transparency, strengthen compliance with pay equity regulations, and support data-driven decision-making in workforce reward strategies, enabling organizations to build more equitable and consistent compensation frameworks aligned with organizational goals.
DOI: https://doi.org/10.5281/zenodo.19128496
