Optimization of Support Vector Machine for Imbalanced Credit Risk Classification
TL;DR
The study demonstrates that credit-risk models should be selected based on balanced default detection rather than overall accuracy alone and demonstrates that the SVM model trained without standardization failed to identify default clients effectively.
Abstract
Credit risk classification is an important component of financial decision-making because inaccurate classification may increase payment-default exposure and reduce lending quality. Credit datasets commonly contain numerical variables with different measurement scales and imbalanced distributions between default and non-default clients, which can affect the reliability of machine-learning models. Objective: This study aims to develop and evaluate a Support Vector Machine model for classifying credit card clients into default and non-default categories. The study also examines the influence of numerical standardization, kernel selection, hyperparameter optimization, and balanced class weighting on classification performance. Methodology: A quantitative experimental approach was applied using the Default of Credit Card Clients dataset from the UCI Machine Learning Repository. The dataset consisted of 30,000 observations and 23 predictor variables. Data were divided into training and testing subsets using a stratified 80:20 ratio. Categorical variables were encoded, numerical variables were standardized, and several SVM kernels were evaluated. Hyperparameter selection was conducted using five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, specificity, balanced accuracy, and ROC–AUC. Findings: The SVM model trained without standardization failed to identify default clients effectively. Numerical standardization substantially improved classification performance, while the radial basis function kernel produced the strongest validation results. The selected balanced RBF-SVM achieved 77.13% accuracy, 48.54% precision, 56.22% recall, 52.09% F1-score, 83.07% specificity, 69.65% balanced accuracy, and 75.10% ROC–AUC. Balanced class weighting improved default detection but increased false-positive predictions. Implications: The model can support financial institutions as an initial credit-risk screening tool. Its predictions should be combined with document verification, repayment-capacity analysis, and manual assessment rather than being used as the sole basis for credit approval. Originality: This study provides a controlled evaluation of SVM performance by integrating feature standardization, kernel selection, hyperparameter optimization, class-imbalance treatment, and class-sensitive performance metrics. The study demonstrates that credit-risk models should be selected based on balanced default detection rather than overall accuracy alone.