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@article{197197,
        author = {Ajay Mishra and Aarya Talekar and Maruf Pathan and Swaraj Dudhmal and Prof. Kaajal Sharma},
        title = {RiskNexus: An Explainable Credit Risk Analysis and Prediction System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5500-5506},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197197},
        abstract = {RiskNexus is an explainable credit-risk analysis platform designed for sustainable consumer lending. It combines a Random Forest model with transparent rule-based heuristics to estimate a FICO-like credit score using key financial features such as income, debt, and credit history. If the ML model is unavailable, a domain-based scoring system ensures continuity. The platform includes a customer dashboard for score simulation and recommendations, along with a bank-focused module for default risk and credit grading. It also supports “what-if” analysis to show how user actions impact credit scores. This hybrid approach balances accuracy, robustness, and interpretability for effective decision support.},
        keywords = {Credit Risk Prediction, Machine Learning, XGBoost, Support Vector Machine, Financial Analytics, Loan Default Prediction, Predictive Modeling.},
        month = {April},
        }