This paper analyzes the evolution of mobile banking technologies and their impact on the global banking industry. It compares traditional banking systems, characterized by SMS, USSD, and basic application functionalities, with next-generation, AI-powered platforms that incorporate machine learning, natural language processing, and predictive analytics. The analysis is based on technological architecture, user experience, operational efficiency, security mechanisms, and scalability.
Through a combination of literature reviews and case studies from Bank of America, HDFC Bank, and digital-only banks like Revolut, the paper demonstrates how AI-enhanced mobile banking is improving personalization, enabling near real-time customer support, intelligent fraud detection, and data-driven decision-making. In contrast, traditional systems are valued for their simplicity, minimal technological requirements, and reliability in low-connectivity environments.
The study traces the development of mobile banking from basic systems (SMS and app-based transactions) to AI-driven solutions, including chatbots, predictive analytics, and voice-assisted banking. It contrasts these paradigms based on features, user experience, security, adoption, and challenges. The paper concludes that while AI-enhanced mobile banking offers improved personalization, efficiency, and scalability, it also raises concerns regarding data privacy and algorithmic transparency.
International Journal of Research in Finance and Management
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