Modeling a Software Solution for Analyzing Customer Complaints in the Banking Sector

The quick advancement in the creation of digital banking systems results in the emergence of more text data, such as complaints, reviews, and service requests, from consumers. The need for systems able to deal with a large amount of data in a sophisticated manner is vital in this situation. Studies show that NLP, together with machine learning, allows financial organizations to gather important information through analyzing texts produced during client-server interactions and thus make better decisions. Automated analysis of complaints made by bank consumers will lead to the identification of service problems and patterns of client discontent. It has been revealed by recent studies that sophisticated classification algorithms and neural network technologies are able to correctly classify complaints made by customers according to predefined categories of services offered by banks, thus minimizing efforts spent on manual analysis and response time. In addition, sentiment analysis techniques make it possible to determine the attitude of customers towards various services provided by banking institutions, which is helpful for effective planning purposes. Topic modeling techniques may also assist in uncovering hidden topics within a set of complaints.