Human resource management in the banking sector involves complex decision-making under uncertainty, requiring the evaluation of multiple quantitative and qualitative criteria. Traditional approaches often rely on subjective expert judgment, which reduces consistency, transparency, and decision accuracy. This study presents the development of an intelligent software-based decision support system for human resource management in banking. The proposed system integrates artificial intelligence techniques, fuzzy logic, and multi-criteria decision-making methods, particularly Fuzzy TOPSIS, to improve the objectivity and efficiency of personnel evaluation. The software was implemented as a full-stack web platform using React and TypeScript for the frontend, Python and Flask for backend analytical processing, and SQLite for data storage. The system supports role-based access, evaluation session management, criteria configuration, expert-based linguistic assessment, and automated ranking of alternatives. Experimental validation using banking HR evaluation scenarios demonstrated that the developed software successfully processed expert assessments, generated reliable candidate rankings, and reduced subjective bias. The proposed system improves decision transparency, increases evaluation speed, and enhances the reliability of human resource management decisions in banking institutions (pp.97-105).