Review of Improving Banking Operational Efficiency through AI and ML: Strategy, Implementation and Impact


  • Idha Kristiana Binus University



Operational Banking Efficiency, Artificial Intelligence, Machine Learning, Systematic Literature Review


Artificial Intelligence (AI) and Machine Learning (ML) are critical components in the financial industry's ongoing transformation of operational dynamics with the aim of improving efficiency. Utilizing a systematic literature review (SLR) approach, this article investigates the implementation and impact of AI and ML in banking. It focuses on the ways in which these technologies can optimize operational processes, the obstacles encountered by banks during their integration, and the potential future developments of these innovations in banking operations. The study evaluates the transformative potential of AI and ML in the banking industry, identifies the obstacles to their adoption, and addresses three research questions by examining the potential of AI and ML to enhance operational efficiency. Our research demonstrates that AI and ML substantially increase productivity by virtue of their sophisticated data processing and decision-making functionalities. Nevertheless, persistent integration obstacles include concerns regarding data security, substantial upfront investments, and deficiencies in expertise in AI and ML. In the future, AI and ML have the potential to significantly transform the finance industry. By proposing three novel use cases automating credit evaluation process, augmenting banking operations via Advanced Optical Character Recognition (OCR) solutions and optimizing data analysis efficiency the paper makes a scholarly contribution. The following recommendations provide banks with actionable insights on how to optimize operational efficiency through the utilization of AI and ML.


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How to Cite

Kristiana, I. (2024). Review of Improving Banking Operational Efficiency through AI and ML: Strategy, Implementation and Impact. Jurnal Komunikasi, Sains Dan Teknologi, 3(1), 270–278.