aspg-38-2402-1792223320 20261017074840 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Sustainable Development and Green Technology JSDGT 2836-5399 2024 4 1 Abedallah Z. Abualkishik American University in the Emirates, Dubai, United Arab Emirates Rasha Almajed American University in the Emirates, Dubai, United Arab Emirates The complexity of the business environment, which is shaped by dynamic economic, environmental, and social factors, makes it more important for companies to develop sustainable risk management practices. This research proposes a novel approach that combines traditional methods with modern machine learning techniques in response to the complex challenges faced by contemporary businesses in balancing risk and sustainability. Our study uses a public bank loan default dataset as a case study to address missing data systematically through robust imputation mechanisms and transform categorical variables using feature encoding. Spearman correlation analysis helps us understand complex variable relationships and guides subsequent feature selection. The decision tree classifier, a powerful machine learning algorithm known for its interpretability, is applied to identify key factors contributing to risk assessment. The hierarchical structure of the decision tree not only reveals important variables but also provides an explicit representation of the decision-making process. ROC curve analysis shows how well our predictive model can differentiate potential loan defaults. 2024 01 06 2402 https://creativecommons.org/licenses/by/4.0/ 10.54216/JSDGT.040101 https://americaspg.com/journal/38/article/2402 https://americaspg.com/storage/articles/manuscripts/01M3YPSR50KSGQ172J9VGWKAXR.pdf