Abstract
The advancement in communication technology has posed a significant challenge in realizing individual banking needs, thus the need to design new ways of meeting customers’ needs. This paper examines how NLP and sentiment analysis can improve banking personalization by focusing on customers’ feelings about their banks from chatted conversations, contact points, and social media. With the help of the longitudinal analysis of the emotional tone and the conversational context using superior techniques of NLP, the study implies that clients are more likely to stay loyal to a particular firm if recommended financial products are tailored to meet their needs. The paper also evaluates different sentiment analysis models regarding their effect on personalization precision and functionality. The findings indicate profound patterns that characterize customer behavior and suggest how sentiment-derived information can be incorporated into financial decision-making practices. The findings provide significant insights that financial institutions may find useful as they integrate AI-enabled approaches to enhance customer engagement and deliver personalized services.
Keywords
Personalized Banking, Fintech, Investment Management, Wealth Management, Banking, Insurance, Financial Planning, Financial Consulting, Text Mining, Opinion Mining, Financial Sentiment, Market Sentiment, NLP for Finance
Citations
IRE Journals:
Kalyan C Gottipati , Deepika Maddineni
"Personalized Financial Services Using NLP and Sentiment Analysis" Iconic Research And Engineering Journals Volume 8 Issue 7 2025 Page 587-601
IEEE:
Kalyan C Gottipati , Deepika Maddineni
"Personalized Financial Services Using NLP and Sentiment Analysis" Iconic Research And Engineering Journals, 8(7)
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