Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kapoor, Shalini; Jain, Samkit; Gupta, Praveen Kumar; Vidushi; Bhardwaj, Shashank Title A name given to the resource Integrating Explainability and Fairness in Credit Risk Prediction: A Hybrid Approach Using Tabnet, LightGBM, SHAP, and Counterfactual Explanations on the FICO Dataset Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived 2026 2nd International Conference on Computing, Sciences and Communications, ICCSC 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCSC67078.2026.11468663" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCSC67078.2026.11468663</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038013135?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038013135?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kapoor S., Scset, Bennett University, Greater Noida, India; Jain S., Scset, Bennett University, Greater Noida, India; Gupta P.K., Scset, Bennett University, Greater Noida, India; Vidushi, Christ University, Bengaluru, India; Bhardwaj S., Kiet University, Delhi-NCR, Ghaziabad, India Description An account of the resource Transparent and fair credit risk assessment is essential for responsible lending in modern financial systems. This paper presents an interpretable and ethically grounded machine learning framework for loan default prediction using the FICO Explainability Challenge dataset. We combine LightGBM, a high-performing gradient boosting model for tabular data, with TabNet, a deep learning architecture that provides intrinsic interpretability through attentive feature selection. To enhance transparency, SHapley Additive exPlanations (SHAP) are employed for global and local feature attribution, while counterfactual explanations generated using the DiCE framework offer actionable recourse. Fairness is evaluated and mitigated using IBM's AI Fairness 360 toolkit. Experimental results demonstrate that the proposed hybrid approach achieves strong predictive performance while ensuring interpretability and fairness, making it suitable for trustworthy and regulation-compliant credit risk modeling. 2026 IEEE. Subject The topic of the resource Counterfactual Explanations; Credit Risk Prediction; Explainable AI; Fairness; LightGBM; SHAP; TabNet Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159576-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online