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@article{202220,
        author = {Pallavi Prakash Madarakhandi and Dr. Sakshi Kathuria and Dr. Ekta Soni},
        title = {Credit Risk Prediction With Explainable AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6467-6472},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202220},
        abstract = {The credit risk prediction is a significant aspect of financial risk management since it assists banks and other financial institutions in assessing the chance of a borrower falling to fulfil loan repayment obligations. The right prediction models are required to minimise the financial losses as well as to facilitate good lending decisions. Statistical methods that have been used traditionally in credit risk assessment include logistic regression, discriminant analysis, and classical methods of credit scoring. These methods utilise historical data of both financial and demographic data of a borrower to determine a probable default. As artificial intelligence and machine learning have developed, increasingly sophisticated predictive models have been presented to predict credit risk. Decision trees, random forests, support vector machines, gradient-boosting and neural networks are algorithms that have demonstrated better predictive accuracy than the classical algorithms. These models can extract complicated patterns in high-volume financial data, and this enhances the precision of risk analysis. Nevertheless, a lot of machine learning models are not transparent, and they are black boxes. To solve this problem, Explainable Artificial Intelligence methods like SHAP and LIME can be used to identify the influence of various features on model predictions and, therefore, achieve enhanced interpretability and enable more appropriate financial decisions.},
        keywords = {Credit Risk Prediction, Machine Learning, Explainable AI, SHAP, LIME, Financial Risk Management.},
        month = {May},
        }