<p>Customer behavior is essential to comprehending and managing an organization’s financial operations. It has to do with how customers choose products and services and utilize them. As stated differently, there exists a clear correlation between financial literacy and the sustainable usage of financial products. The purpose of this study was to investigate the numerous implications of financial literacy in education. In this study, we proposed a novel synergistic fibroblast kernelized support vector machine (SF-KSVM) for use with an individualized financial literacy education platform. The data was collected from media networks. This study analyzes emotions with the use of natural language processing (NLP) and statistical analysis. The proposed method is implemented using Python software. The proposed methods are compared to other existing algorithms. Metrics for evaluating performance include Accuracy (98%), Precision (97.5%), Recall (97%), Execution time (1.15&#xa0;m) and F1-Score (97.3%). Findings show the proposed method achieves better performance than the other algorithms. As a result, we suggest that it is critical to underline the need for financial literacy to receive more attention in the financial sector to empower individual consumers to adopt more sustainable behavior.</p>