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@article{177392,
        author = {Shivam Ramteke and Priyansh Maharana and Shreshtha Das and Pratham Singh},
        title = {DigiCred - Alternative Credit Scoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {711-715},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177392},
        abstract = {Traditional credit scoring mechanisms often exclude a significant portion of the population who lack formal financial histories, thereby limiting their access to essential credit services. To address this gap, DIGICRED proposes an AI-powered alternative credit scoring system that leverages non-traditional data such as utility payments, employment stability, rental history, and digital transaction behavior. By applying machine learning techniques—specifically the Random Forest Regressor—DIGICRED generates a numerical credit score that reflects the financial trustworthiness of individuals typically ignored by conventional models. The system utilizes a carefully constructed dataset with both clean and realistic outlier entries to enhance predictive accuracy and model generalizability. Evaluation metrics such as MAE, RMSE, and R² Score validate the effectiveness of the proposed solution.},
        keywords = {Credit Scoring, Alternative Credit Scoring, Blockchain, Machine Learning, Random Forest, Financial Risk Assessment, Federated Learning, Explainable AI, Data Security, Credit Risk Mitigation, Artificial Intelligence, Fog Computing.},
        month = {May},
        }