Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{202219,
        author = {Pallavi Prakash Madarakhandi and Dr. Sakshi Kathuria and Dr. Ekta Soni},
        title = {Enhancing Credit Risk Prediction through Explainable Artificial Intelligence and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6454-6466},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202219},
        abstract = {A fundamental business of financial institutions is credit risk identification that has a direct impact on the solvency and strategic decision-making. Although the traditional statistical models are interpretable, they are not usually able to predict the non-linear complexities of the modern financial data. The study hypothesis is that there is a powerful machine learning-based credit default prediction algorithm which takes a credit card client dataset. The high-level preprocessing, feature engineering, and Synthetic Minority Oversampling Technique (SMOTE) are employed to address the class imbalance problem in the methodology. We perform a comparative study of the Logistic Regression, decision trees, random forest, and extreme gradient boosting (XGBoost). The XGBoost model is the best model as it has an accuracy and ROC-AUC of 85.72 percent and 0.857, respectively. In a bid to make sure that we abide by the rules and develop trust in the institutions, we use Explainable AI (XAI) with SHAP and LIME and convert black-box predictions into transparent and actionable explanations.},
        keywords = {Credit Risk Assessment, Machine Learning, Explainable AI, Predictive Analytics, Financial Risk Management.},
        month = {May},
        }