Provide an Opinion Analysis-Based Recommender System for Personalized Personal Banking Services

Customer retention is an important issue for banks. This issue in the field of machine learning and statistical issues is focusing on the problem of accurately predicting customer needs and responding to it in the bank’s dynamic environment. Since rarely effective action has been taken by considering personalized actions to improve customer retention rates, but these decisions are at least as important as the proper identification of endangered customers. Deciding about what to do to provide specific and personalized services for customers typically leads to managers who can only rely on their knowledge. By reviewing the literature on CRM, this research provides a model that can be used to generate customer retention and marketing activities in personal banking, which includes analyzing clients and producing actions to maintain them, and analysts also set Using personalized actions to maintain customers using one of the approaches presented in this article, they are using an recommender system utilizing the opinion and experiences of customers.