This study develops and evaluates a machine learning-based model to predict online reputational dimensions of financial entities in Ecuador, using a comprehensive approach that incorporates both traditional and novel computational techniques. The research leverages the power of Support Vector Machines (SVM) and the Synthetic Minority Over-sampling Technique (SMOTE) to effectively handle the complexities of text data and class imbalances. The selected model excels in performance metrics such as accuracy, precision, recall, and F1-score, making it a promising tool for continuous and real-time reputation monitoring in the banking sector. Additionally, the paper discusses the implications of these technologies for proactive reputation management, addressing the need for dynamic strategies in the evolving digital communication and stakeholder interaction. The study sets a foundation for future work, suggesting integrating more complex deep learning models and semi-supervised learning techniques to enhance the model’s robustness and operational efficiency.