Financial institutions rely heavily on loan risk assessments, and lending decisions cannot be made without prediction models. Using a Feed-Forward Neural Network algorithm, we present a practical method for predicting loan risk in this article. In this research, we compare and contrast the FNN algorithm with SVMs. When predicting potential dangers, SVM is a popular tool. Finding out how well the FNN algorithm predicts defaults is the primary goal of this research. The results show that FNN is more accurate than SVM, which is encouraging. This highlights the FNN’s potential to transform the way loan risk assessment is done. The significance of using neural networks to improve the trustworthiness and precision of loan risk prediction systems is shown by our findings. The FNN model is well-suited for risk assessment based on its exceptional performance. Data scientists can use the findings of this study to enhance accuracy and, in the long run, cut down on financial losses caused by defaults. After reviewing the literature, we have come to the conclusion that the FNN algorithm could revolutionize risk prediction in the financial sector. Better loan decisions can be made with the help of additional research and experiments that expand the FNN models applicability to other financial applications.