FairCred — An Explainable, Bias-Mitigated Deep-Learning Framework for Real-Time Loan Approval Using Alternative Data

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{181458,
        author = {Vishal V Arakeri and Sindhu and Mohamed Ahmed hussain and Sowmya Gaitond},
        title = {FairCred — An Explainable, Bias-Mitigated Deep-Learning Framework for Real-Time Loan Approval Using Alternative Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3972-3977},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181458},
        abstract = {},
        keywords = {Credit risk, alternative data, deep learning, artificial neural network, fairness, SHAP explainability, microservice deployment, loan underwriting.},
        month = {June},
        }

Cite This Article

Arakeri, V. V., & Sindhu, , & hussain, M. A., & Gaitond, S. (2025). FairCred — An Explainable, Bias-Mitigated Deep-Learning Framework for Real-Time Loan Approval Using Alternative Data. International Journal of Innovative Research in Technology (IJIRT), 12(1), 3972–3977.

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