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@article{207299,
        author = {Prof. Somesh Kumar Shukla and Ramakant singh},
        title = {Does Artificial Intelligence Make Credit Decisions More Equitable? “An Analytical Study of Creditworthiness Models in the Indian Context},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1004-1019},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207299},
        abstract = {The present study explores, specifically in the Indian context, whether Artificial Intelligence (AI)-based creditworthiness models prove more helpful in equitable decision-making processes compared to traditional credit-assessment systems. This research adopts a mixed-method approach, which includes conceptual analysis along with a systematic and comparative review of empirical studies of AI/ML-based credit scoring models. This study clarifies that Artificial Intelligence (AI)-based models, by using high-dimensional and alternative data, significantly increase predictive accuracy and make credit risk assessment more robust. However, the analysis also reveals that improvement in predictive capability does not necessarily mean more equitable credit outcomes. The study underlines that AI-driven systems prioritize those borrowers whose financial profiles are rich and stable, which can create adverse conditions for individuals with limited credit history or weak digital footprints. This research identifies key challenges in the path of adopting AI, such as algorithmic bias, lack of transparency, excessive dependence on data, and governance constraints. These factors collectively determine the distributional impact of credit decisions, proving the limitations of relying solely on efficiency metrics like accuracy or AUC. In the Indian context of financial informality and unequal digital access, this study indicates that AI-based systems, while expanding inclusion for some groups on one hand, can further reinforce exclusion for other groups on the other. Ultimately, this research concludes that the efficiency of AI in credit decisions is indisputable, but its contribution to ensuring equity depends entirely on data representativeness, model design, and the strength of the regulatory and governance frameworks.},
        keywords = {Artificial Intelligence, Machine Learning, Credit Scoring, Algorithmic Fairness, Financial Inclusion, Credit Risk, India},
        month = {August},
        }