COMPARATIVE EVALUATION OF CREDIT RISK AND LOAN DEFAULT PREDICTION MODELS BY MACHINE LEARNING.
TL;DR
By transforming applicant-related financial and demographic information into predictive risk estimates, the proposed approach provides a systematic framework for loan default assessment and demonstrates the applicability of machine learning-based classification techniques to credit risk analysis.
Abstract
Identifying borrower default is a critical challenge in credit risk management, as inaccurate prediction can lead to financial losses and inefficient lending decisions. This study presents a comparative machine learning approach for loan default prediction using the Home Credit Default Risk dataset, which contains financial, demographic, employment, and credit-related information of loan applicants. Four classification algorithms, namely Logistic Regression, Gradient Boosting, XG Boost and SVM, are evaluated for their ability to classify applicants according to default risk. The proposed methodology involves feature selection, data preprocessing, median imputation for missing numerical values, categorical data handling, stratified data splitting, model training, and performance evaluation. Fourteen predictive variables related to income, credit amount, annuity, employment, demographic characteristics, vehicle ownership, and external credit scores are considered. The study also addresses class imbalance, as the underrepresentation of default cases may make accuracy alone insufficient for evaluating classification performance. Therefore, accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC are considered for comprehensive model evaluation. The comparative framework analyzes the performance of the selected classification techniques using consistent preprocessing and evaluation criteria. By transforming applicant-related financial and demographic information into predictive risk estimates, the proposed approach provides a systematic framework for loan default assessment. The study demonstrates the applicability of machine learning-based classification techniques to credit risk analysis and provides a structured approach for evaluating potential borrower default risk. The findings can support data-driven credit assessment and assist lending institutions in developing more systematic approaches to loan risk evaluation and decision-making.