This study applies machine learning (ML) techniques to predict default risk among disabled adults in South Korea, utilizing data from the Panel Survey of Employment for the Disabled (PSED). Disabled individuals face unique socioeconomic challenges, including limited employment opportunities, inadequate social support, and significant medical expenses, which contribute to financial instability and heightened default risk. Existing financial risk models typically overlook these multifaceted aspects of disability, resulting in a critical gap in accurately assessing default risk within this population. Our research aims to develop predictive models specifically tailored to the socioeconomic backgrounds of disabled adults, enhancing the precision of default risk assessments._x000D_ The methodology involves comprehensive preprocessing of PSED data, such as demographics, income levels, disability type and severity, and employment status. We employ various machine learning algorithms, including logistic regression, random forests, and extreme gradient boosting, validated through cross-validation and performance metrics. Preliminary results indicate that ensemble learning models, especially extreme gradient boosting, exhibit superior predictive performance. Key predictors of default risk include employment, disability severity, and dependence on social support._x000D_ The findings could inform financial institutions and policymakers by highlighting overlooked factors in risk assessments and promoting inclusive financial practices that support the economic well-being of disabled individuals.
