Fuzzy Shadowed Support Vector Machine for Bankruptcy Prediction

Abstract

Corporate defaults represent a critical risk factor for financial institutions and stakeholders. In today’s complex economic environment, precise and timely risk assessment has become an essential component of financial strategies. One promising strategy consists of analyzing and learning from financial patterns observed in distressed or bankrupt firms. However, this requires processing highly imbalanced datasets in which bankruptcy cases are substantially underrepresented relative to solvent firms. This imbalance, coupled with the data’s intrinsic complexity—such as overlapping features and nonlinear patterns, poses significant difficulties for traditional classifiers like Support Vector Machines (SVMs), which tend to favor the majority class. To overcome these challenges, we employ a Fuzzy Shadowed SVM, which allows for a more refined modeling of minority class instances. This method leverages granular computing paradigms to enhance predictive robustness. Empirical results based on real-world datasets show that our model significantly outperforms traditional machine learning approaches, particularly in recognizing minority-class instances.