Machine Learning Approaches to Creditworthiness Classification

Abstract

Credit risk evaluation is fundamental in financial decision-making, directly influencing lending strategies and default prevention. With the growing availability of structured financial data, machine learning methods have become increasingly prominent in building predictive credit scoring models. This research evaluates Decision Tree, Random Forest, and SVM classifiers for creditworthiness assessment. The Statlog (German Credit Data) dataset from UCI is used with a standardized preprocessing pipeline. Each model was trained and tested on the same dataset split and evaluated using standard classification metrics such as accuracy, precision, recall, and F1 score. Results show that the Random Forest classifier achieved the highest overall performance, particularly in identifying good credit applicants. At the same time, the Decision Tree maintained interpretability, and SVM offered a balanced trade-off. The findings highlight key considerations for model selection in credit scoring applications and suggest ensemble methods as strong candidates for future deployment.