Natural Language Processing for Automated Customer Service in Banking

The fast development of digital technologies has tidily altered the sphere of customer service in banks. Natural Language Processing (NLP) is one of such innovations which can effectively help automate the process of analyzing customer feedback. The paper at hand identifies how NLP techniques, especially Term Frequency-Inverse Document Frequency (TF-IDF) and machine learning models, namely Random Forest (RF) and Logistic Regression (LR), can be used to determine the customer sentiment (positive, negative, and neutral). A balanced and preprocessed dataset is used to vectorize the text data and divide it into training and testing sets. The models were trained with cross-validation on hyperparameters tuning, and on all evaluation metrics: accuracy, precision, recall, and F1-score, they show perfect classification. The insights regarding the model predictions were also supported by data visualization such as sentiment distribution and word clouds. Although results prove the great efficiency of these models, such possible limitations as overfitting and the necessity of more complicated models in real-life applications are also discussed in the study. On the whole, the results demonstrate the transformative nature of NLP when it comes to automated customer service and customer experience in the banking domain.

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