Development of an Intelligent Decision Support Model for Multi-Criteria Evaluation of Bank Employees Using the Fuzzy TOPSIS Method

Although decision-making in human resources within the banking sector increasingly requires a data-driven and transparent approach, subjectivity remains a major problem in current evaluation practices. This article proposes an intelligent decision support model for the quarterly appraisal of employees in bank human resource management, based on fuzzy multi-criteria decision-making and the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution). To address this problem, a system of criteria has been developed, including accuracy, productivity, teamwork and collaboration, innovativeness, attendance, number of completed projects, level of code optimization, degree of software correctness, creative project solutions, and adoption of new technologies. These criteria combine both quantitative indicators and qualitative measures expressed linguistically by HR specialists and managers. To model the uncertainty inherent in linguistic assessments, trapezoidal fuzzy numbers and membership functions are employed to construct a fuzzy decision matrix. By incorporating criteria weights and experts’ competence coefficients, the fuzzy TOPSIS procedure evaluates each employee’s performance according to the distances from the fuzzy ideal and antiideal solutions; the closeness coefficient is then computed and the alternatives are ranked accordingly. A representative application in the banking sector indicates that the proposed model standardizes the appraisal process, enhances transparency, and reduces the influence of subjective bias in decisions such as compensation and promotion. The findings also suggest that a fuzzy TOPSIS–based approach can be effectively integrated into banks’ human resource information systems as an intelligent decision support module.