DESIGN AND DEVELOPMENT OF SMART POS SYSTEMS: COMMISSION OPTIMIZATION AND TRANSACTION EFFICIENCY

Purpose- Traditional POS systems often lead to high transaction costs, increased error rates, and insufficient customer satisfaction. To address these limitations, smart POS systems have been developed to optimize transaction processes using advanced routing algorithms. This study aims to examine the impact of smart POS systems on transaction efficiency, customer satisfaction, commission cost reduction, and their influence on employee task allocation towards more strategic and value-added roles through intelligent routing and data-driven decision mechanisms. Methodology- Dynamic routing mechanisms were utilized for commission cost optimization. Big data analytics tools, including Apache Spark and HDFS, were applied to process real-time transaction data, ensuring scalable and adaptive solutions. Findings- The findings indicate that smart POS systems significantly reduce commission costs, lower transaction error rates, and improve operational efficiency. Additionally, integrating customer behavior prediction models with intelligent routing strategies enhances customer satisfaction and financial performance. The results further suggest that automation and data-driven approaches reduce manual interventions in transaction processes, enabling employees to allocate more time to strategic roles and value-added tasks. This shift contributes to organizational adaptability and innovation. Conclusion- This study highlights the contributions of smart POS systems to the fintech sector by addressing gaps in transaction efficiency, commission cost management, and customer satisfaction. The findings demonstrate that data-driven decision-making and dynamic routing mechanisms not only optimize transaction processes but also positively impact customer experiences, financial outcomes, and employee engagement in strategic tasks. Keywords: Smart POS systems, transaction efficiency, commission cost optimization, customer behavior prediction, big data analytics JEL Codes: D53, G21, L86