<![CDATA[This study develops a predictive model for loan default in Indonesia’s multifinance industry by implementing and comparing three machine learning methods: Logistic Regression, Support Vector Machine (SVM) with RBF kernel, and XGBoost. Using imbalanced datasets from three multifinance companies representing different portfolio characteristics vehicle, multipurpose, and electronics financing the research applies the SMOTE technique to address class imbalance and enhance model sensitivity. Results show that XGBoost outperforms both Logistic Regression and SVM in accuracy (0.9970), precision (0.9482), recall (0.9987), and AUC (0.9996), while also being the most computationally efficient. Feature importance analysis highlights late payment history, financial ratios, credit scores, and demographic variables as key predictors, with XGBoost capturing complex non-linear interactions. The study introduces a novel multi-layered framework for credit risk management, including scoring engines, early warning systems, and segment-based risk strategies. Segment analysis reveals higher default risks among younger, divorced, and less-educated borrowers, as well as for unsecured loans and high debt-to-income ratios. The model’s adaptability across varying institutional datasets demonstrates the need for company-specific calibration. Compared to previous single-model or single-company approaches, this research provides a comprehensive, scalable, and high-performing solution for predictive credit risk modeling in the Indonesian context. Simulation results suggest that the implementation of this framework could reduce NPF by up to 2.3 percentage points and enhance risk-adjusted returns by 3.8–4.2%, offering substantial practical value to multifinance companies.]]>
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