<![CDATA[Credit is a financial instrument that carries the risk of default when borrowers are unable to fulfill their repayment obligations. This risk may lead to financial losses for lending institutions, making accurate credit risk prediction essential. This study aims to develop a credit risk prediction model using Extreme Gradient Boosting (XGBoost) and point out its predictions using an AI explainer based on SHapley Additive exPlanations (SHAP). The dataset was obtained from the internal database of a fintech company in Indonesia and consisted of 116,250 records with 19 attributes. The research stages included Exploratory Data Analysis (EDA), missing value and outlier handling, data splitting, model training, evaluation, and result interpretation. The XGBoost model achieved an accuracy of 92%. For the Less Current class, the model obtained a precision of 0.91, a recall of 0.97, and an F1-score of 0.94. For the current class, the model achieved a precision of 0.86, a recall of 0.62, and an F1-score of 0.72. For the Default class, the model obtained a precision of 0.97, a recall of 0.91, and an F1-score of 0.94. SHAP analysis as an AI explainer revealed that the income and side_income features were the most influential factors in determining credit risk.]]>
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