Extracted features.

The adoption of Financial Technology (FinTech), along with the enhancement of Human Resource (HR) competencies, service innovation, and firm growth, plays a crucial role in the development of the banking sector. Despite their importance, obtaining reliable results is often challenging due to the complex, high-dimensional correlations among various features that affect the industry. To address this issue, this research introduces a hybrid Multi-Criteria Decision-Making (MCDM) model that integrates the Entropy-Weighted Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The primary objective of this study is to systematically evaluate and rank multiple alternatives based on key criteria using the EWM-TOPSIS approaches. Specifically, the analysis considers eleven multifaceted characteristics and eight potential alternatives (A1 to A8), revealing the significant influence of the proposed MCDM approaches in assessing FinTech adoption, HR competency, service innovation, and firm growth. The findings underscore the effectiveness of the entropy-TOPSIS approaches in providing a structured analysis for a smarter and well-informed decision-making. Ultimately, this research proposes the best alternative from the evaluated options, contributing valuable insights into the future role of FinTech, HR competencies, service innovation, and firm growth within the banking sector.