Artificial Intelligence Integration in Existing Payment Systems: Challenges and Addressed Strategies for Operational Optimization in Tanzanian Financial Institutions

Purpose: This study examined the integration of Artificial Intelligence (AI) into Tanzanian payment systems and its contribution to operational optimisation. It focused on how AI improves transaction efficiency, enhances fraud detection, and supports financial inclusion while identifying key implementation challenges. Design/Methodology/Approach: The study used a mixed approach combining a Systematic Literature Review (SLR) and a questionnaire survey. Relevant academic studies, regulatory reports, and institutional publications were reviewed to identify AI integration trends, benefits, and challenges. Questionnaire data provided quantitative insights into operational efficiency and system performance. Findings: AI improves transaction accuracy, reduces processing time, enhances fraud detection, and promotes financial inclusion. However, challenges such as legacy infrastructure, data fragmentation, regulatory uncertainty, and infrastructural limitations affect implementation. Strategies such as phased implementation, hybrid system integration, and robust data governance help address these barriers. Implications/Originality/Value: The study provides guidance to financial institutions and regulators on implementing AI solutions to improve efficiency, security, and reliability in Tanzania’s payment systems.