Authors: Miss. Tatipaka Pooja, Miss. Savarapu Suhasini
Abstract: Accurate credit risk assessment is essential for financial institutions to minimize loan defaults and support effective lending decisions. Conventional loan evaluation processes largely depend on manual analysis of customer financial information, making them time-consuming, inconsistent, and susceptible to human bias. With the rapid advancement of machine learning, intelligent prediction models have emerged as efficient solutions for automating credit risk evaluation and improving decision-making accuracy. This paper presents an intelligent credit risk prediction framework that utilizes machine learning algorithms to classify loan applicants based on their probability of loan repayment or default. The proposed framework analyzes customer financial and demographic attributes, including credit history, checking account status, employment status, loan amount, loan duration, and applicant age. Data preprocessing techniques such as missing value handling, outlier removal, categorical feature encoding, and feature scaling are employed to enhance data quality before model training. Multiple machine learning algorithms, including Decision Tree, Random Forest, Support Vector Machine (SVM), Naïve Bayes, Multi-Layer Perceptron (MLP), and a Stacking Ensemble model, are implemented and comparatively evaluated using performance metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC analysis. Experimental results indicate that the ensemble learning approach consistently outperforms individual classifiers by achieving higher prediction accuracy and improved generalization capability. The proposed framework provides a reliable, scalable, and data-driven solution for intelligent credit risk assessment, enabling financial institutions to improve loan approval decisions, reduce financial losses, and strengthen overall credit risk management.
