<![CDATA[Loan default prediction is an important aspect of risk management in financial institutions. Accurate prediction models enable banks and lending organizations to mitigate risks, allocate resources effectively, and optimize decision-making processes. This study investigates the application of decision tree algorithms in predicting loan default risk in the financial sector. Decision trees are renowned for their interpretability, adaptability to non-linear data, and ability to handle missing values, making them a valuable tool in credit risk analysis. Using a dataset consisting of borrower profiles, credit scores, income levels, and payment history, the model identifies key predictors that influence default outcomes. The study uses the C4.5 decision tree model, which will demonstrate that decision trees achieve high prediction accuracy and offer a transparent decision-making framework, enhancing their applicability in real-world scenarios. Furthermore, the paper highlights the implications of these findings for financial institutions, emphasizing the scalability and cost-effectiveness of the model. By integrating decision tree-based models into existing risk assessment systems, lenders can proactively manage loan portfolios and reduce default rates. Future research directions are proposed to explore hybrid approaches that combine decision trees with advanced combined methods to enhance predictive capabilities. The potential of decision tree algorithms in transforming credit risk assessment and supporting more accurate data-driven financial decision-making processes]]>
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