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@article{194618,
        author = {Swati Tripathi and Dr. Sabeeha Fatima},
        title = {Role of Artificial Intelligence in Credit Risk Assessment},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5078-5083},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194618},
        abstract = {Credit risk assessment is a critical function for banks and financial institutions as it helps determine the likelihood that a borrower may default on loan obligations. Traditionally, credit evaluation has relied on statistical models, financial ratios, and manual assessment processes. However, the rapid growth of digital financial services and the availability of large volumes of data have exposed limitations in traditional credit scoring methods. In this context, Artificial Intelligence (AI) has emerged as an important technological advancement in modern credit risk management.
This research paper examines the role of Artificial Intelligence in enhancing credit risk assessment practices. AI techniques, particularly machine learning algorithms, enable financial institutions to analyse large and complex datasets, identify hidden patterns, and improve the accuracy of default predictions. The study highlights that AI-driven systems contribute to faster loan processing, improved operational efficiency, better fraud detection, and enhanced financial inclusion through the use of alternative data sources.
The research is based on a descriptive and analytical approach using secondary data collected from academic literature, industry reports, and financial publications. The findings suggest that while AI significantly improves the effectiveness of credit risk assessment, challenges related to data privacy, transparency, and regulatory compliance must be carefully addressed. The study concludes that responsible integration of AI with human oversight can strengthen credit decision-making and improve overall financial stability.},
        keywords = {Artificial Intelligence, Credit Risk Evaluation, Machine Learning Models, Financial Risk Analysis, Digital Lending Systems.},
        month = {March},
        }