Abstract: The rapid digital transformation of the Indian banking sector has significantly increased the demand for intelligent, inclusive, and efficient customer support systems, particularly in public sector banks (PSBs). India’s linguistic diversity poses a major challenge for traditional customer interaction mechanisms that rely primarily on English-centric interfaces. This research paper proposes the design and development of a smart Natural Language Processing (NLP)-driven multilingual customer interaction module tailored specifically for public sector banks in India. The proposed module integrates advanced NLP techniques such as language detection, speech-to-text processing, intent classification, sentiment analysis, and multilingual response generation to enable seamless communication between banks and customers across regional languages. The study adopts a design-oriented methodology supported by a review of recent Indian research in banking automation, AI-driven customer service, and multilingual NLP systems. The paper outlines clear objectives, system architecture, and functional components of the module, emphasizing financial inclusion, service quality improvement, and operational efficiency. Discussion and analysis highlight the potential impact of the module on customer satisfaction, grievance redressal, and accessibility for rural and semi-urban populations. The findings suggest that NLP-based multilingual systems can significantly enhance customer experience while reducing workload on bank staff. The study concludes by recommending phased implementation and future integration with core banking systems and regulatory compliance frameworks.

Keywords: NLP, Multilingual Banking, Public Sector Banks, Customer Interaction, Artificial Intelligence

Downloads: | DOI: 10.17148/IARJSET.2026.13134