Enhancing Customer Service in Financial Institutions Through Automated Complaint Classification: A Machine Learning Approach

Authors

  • Doreen Mutesi University of Lay Adventists of Kigali
  • Dr. Jonathan Ngugi University of Lay Adventists of Kigali
  • Dr. Sumbiri Djuma University of Lay Adventists of Kigali

DOI:

https://doi.org/10.70619/vol5iss8pp61-71

Keywords:

Natural Language Processing (NLP), Term Frequency-Inverse Document Frequency (TF-IDF)

Abstract

This paper presents a method to automatically group financial consumer complaints by the type of product they are about, using Natural Language Processing (NLP). The dataset was cleaned and prepared by removing unnecessary words, breaking it into tokens, and converting it into numbers using TF-IDF. Then, Machine learning models are trained to predict which product each complaint is related to. Among the models tested, Stochastic Gradient Descent gave good results. The findings show that NLP can help financial institutions handle complaints faster and more accurately by sorting them automatically into product categories. This approach can be helpful for banks and regulators in improving how they respond to customer issues.

Author Biography

Doreen Mutesi, University of Lay Adventists of Kigali

Faculty of Computing and Information Sciences

References

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Published

2025-09-23

How to Cite

Mutesi, D. ., Ngugi, D. J. ., & Djuma, D. S. . (2025). Enhancing Customer Service in Financial Institutions Through Automated Complaint Classification: A Machine Learning Approach. Journal of Information and Technology, 5(8), 61–71. https://doi.org/10.70619/vol5iss8pp61-71

Issue

Vol. 5 No. 8 (2025)

Section

Articles

License

Copyright (c) 2025 Doreen Mutesi, Dr. Jonathan Ngugi, Dr. Sumbiri Djuma

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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