Performance Comparison of Classical Machine Learning Algorithms for Credit Risk Prediction

Credit risk prediction is a cornerstone task in financial analytics, enabling lending institutions to evaluatethe probability that a borrower will default on an obligation. As financial data grows in volume andcomplexity, machine learning (ML) algorithms have emerged as powerful alternatives to traditionalstatistical scoring models. This paper presents a systematic empirical comparison of seven classicalsupervised machine learning algorithms — Logistic Regression, Decision Tree, Random Forest,Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes —applied to a benchmark credit risk dataset. Each algorithm is evaluated across five performance metrics:Accuracy, Precision, Recall, F1-Score, and AUC-ROC. Experimental results demonstrate that ensemblemethods, particularly Gradient Boosting, achieve superior predictive performance (AUC-ROC = 0.941),followed closely by Random Forest (AUC-ROC = 0.923). Logistic Regression remains a strong andhighly interpretable baseline (AUC-ROC = 0.871). This study provides actionable insights for financialpractitioners and data scientists selecting appropriate ML models for credit scoring applications, anddiscusses trade-offs between interpretability, computational cost, and predictive accuracy.