Comparative Temporal Analysis of SVM-Based Machine Learning Techniques for Bank Risk Assessment

Abstract

This paper introduces an innovative approach that utilizes support vector machines (SVMs) to predict the bankruptcy of financial institutions within the United States. The study aims to identify influential factors that contributed to bankruptcy during two significant peri-ods: the 2008 financial crisis and post-2013. The goal is to highlight both shared and distinc-tive characteristics between these time frames. The proposed method incorporates a meticu-lous feature selection procedure to identify the most critical variables for assessing a banks financial stability. Subsequently, the SVM model is fed with data containing these key vari-ables from various banks, initiating both the training and testing phases. Specifically, two SVM models were trained: one utilizing a linear kernel, and the other employing a non-linear kernel. The objective was to assess their effectiveness in distinguishing between solvent and insolvent banks. Moreover, a neural network model was developed and subjected to a com-parative analysis alongside the aforementioned SVM models, all with the aim of identifying the optimal method for bankruptcy prediction. The training dataset comprised data from the ten quarters preceding bank failures post-2013, as well as the eight quarters leading up to bank failures in 2010, during 2008 financial crisis. The SVM models were implemented us-ing Scikit-Learn, while the neural network model was trained using PyTorch. Through this comprehensive approach, the paper contributes to the advancement of predictive methodolo-gies for identifying potential financial institution bankruptcies.