Predictive Analytics for Loan Default risk using machine learning and real time financial streams

Abstract

Credit risk assessment has moved beyond static scoring to real-time, data-driven analytics. By integrating diverse data (transactions, behavior, macroeconomic factors), modern ML models continuously evaluate borrower creditworthiness, enabling early warnings of default. Compared to legacy methods, this approach yields substantial predictive gains. We compare several classifiers—logistic regression, SVM, decision trees, random forest, and XGBoost—for loan default prediction. Ensemble tree-based methods consistently outperform others, delivering higher accuracy, precision, and F1 scores. In particular, Random Forest achieved the highest predictive performance, showing strong F1 and precision in identifying risky loans. Across metrics (accuracy, recall, AUC), this ensemble approach surpasses simpler models, as benchmarks confirm. Notably, one study found Random Forest anticipated ~12.7% of high-risk client transitions early and helped avert ~67.6% of potential losses. These findings suggest that a Random Forest–based model provides the most reliable credit-risk forecasts, enhancing decision-making and enabling cost savings in risk management. Crucially, these predictive improvements reinforce evidence-based lending: advanced ML models are noted to instill greater confidence in lenders by providing data-driven insights. By reducing misclassification of risky loans, they directly translate into cost savings and stronger overall financial stability. Ultimately, our results demonstrate that choosing the right ML model is key to robust credit risk management.