Natural Language Processing for Automated Customer Service in Banking

Authors

  • Manzoor Anwar Mohammed Oracle EBS Developer, Chicago Public Schools, 42 W Madison St, Chicago, IL – 60602, USA

DOI:

https://doi.org/10.18034/gdeb.v10i2.759

Keywords:

Natural Language Processing, Automated Customer Service, Banking, Chatbots, Machine Learning, Sentiment Analysis, NLP Applications, AI in Banking

Abstract

NLP integration into automated banking customer care systems is examined for its effects on customer experience and operational efficiency. The main goals are to assess NLP's effects on customer happiness, reaction times, and accuracy and identify its limits. Secondary data compares satisfaction levels, response times, and mistake rates before and after NLP's implementation. NLP enhances customer satisfaction and operational efficiency, particularly for routine queries and transactions. Complex problems and technical aid need human intervention, making management challenging. Privacy and data security are also important. Policy implications advise strong data security and a balanced strategy combining automated and human help to answer complex inquiries. NLP can improve banking customer service. However, the research suggests more development and deliberate policy implementation.

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References

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Published

2021-12-31

Issue

Vol. 10 No. 2 (2021): July - December Issue

Section

Articles

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Copyright (c) 2021 Manzoor Anwar Mohammed

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How to Cite

Mohammed, M. A. (2021). Natural Language Processing for Automated Customer Service in Banking. Global Disclosure of Economics and Business, 10(2), 117-128. https://doi.org/10.18034/gdeb.v10i2.759