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 Balaji, K.; Rao, P. Seshagiri Title A name given to the resource Machine Learning-Based Credit Scoring for Personalized and Inclusive Lending in Consumer-Centric Financial Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCIST67338.2025.11438375" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCIST67338.2025.11438375</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037455739?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037455739?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 Balaji K., Christ University, Bengaluru, India; Rao P.S., The Apollo University, School of Technology, Andhra Pradesh, Chittoor, India Description An account of the resource Traditional approaches to credit-scoring are largely based on rule systems that can be excessively fixed and limited to the ability to reflect individual financial behavior. The article analyzes the effectiveness of machine-learning (ML)- based credit ranking with the hypothesis that they can improve predictive capability and fairness of consumer credit lending. The performance of these algorithms, including supervised methods of learning, e.g., logistic regression, random forests as well as the deep learning, is contrasted to the conventional credit models. Model transparency is provided by SHAP values and other methods explainable by AI. Findings show that practice based on the use of ML outperform traditional methods in risk assessment, especially, through the inclusion of supplementary forms of data in traditional databases based on transaction behavior, virtual footprints, and psychometric signals. Furthermore, ethical standards and moral confidence in ML informed credit decision-making will require regulation-proof and explanatory modelling. Through the research, it is recommended to implement policy measures intended to cause financial institutions, fintech companies, and regulating bodies to implement ML-based credit-scoring technologies, with fairness and predictive effectiveness being reciprocal drivers of financial access and consumer-friendly lending practices. 2025 IEEE. Subject The topic of the resource Credit Scoring; Explainable AI; Financial Inclusion; Machine Learning; Personalized Lending Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159676-7; 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