Validation of the Banking Services Redundancy Model in Sepah Bank

This study aims to validate a banking service redundancy model for Sepah Bank to assess the impact of redundant banking services on operational efficiency, customer experience, and technological integration. The research employs a mixed-methods approach, integrating both qualitative and quantitative analyses. The qualitative phase involves a meta-synthesis of existing literature on banking service redundancy, digital banking optimization, and financial risk management. The quantitative phase utilizes survey data collected from 384 Sepah Bank customers and experts in banking service management. Structural equation modeling (SEM) and statistical validation techniques, including Cronbach’s Alpha, composite reliability, average variance extracted (AVE), and Fornell-Larcker criteria, are used to measure construct validity and reliability. Additionally, R² values, effect size (f²), and predictive relevance (Q²) are analyzed to assess the structural model's fit. The results indicate that excessive banking service redundancy leads to operational inefficiencies, increased costs, and diminished customer satisfaction. The structural model analysis revealed a strong R² value (0.996) for banking service redundancy, confirming the robustness of the validation model. The impact of redundancy on customer experience was found to be significant, aligning with prior studies on digital banking efficiency. Moreover, security redundancies, though necessary for regulatory compliance, were identified as a source of usability challenges. The study also highlights the role of AI and data analytics in optimizing banking service redundancy while maintaining regulatory and operational requirements. The study underscores the necessity of a structured validation framework to manage banking service redundancy effectively. Financial institutions must differentiate between operational redundancies that hinder efficiency and regulatory redundancies that are required for compliance. Leveraging AI-driven automation and predictive analytics can help banks streamline redundant services while maintaining security and customer satisfaction. The findings contribute to the broader discourse on digital banking optimization and offer practical insights for financial institutions seeking to enhance operational resilience and service efficiency.