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 Book Chapter Faculty Publications- Book Chapter 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 Malhotra, Shikha; Dhanya, K.A.; Prathibha, Raj M.S.; Mohanty, Priyakrushna Title A name given to the resource AI-Driven Credit Assessment in Banks and Non-Banking Finance Companies (NBFCs) in India: A Comprehensive Analysis 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 Machine Learning and Modeling Techniques in Financial Data Science;pp.275-292 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3693-8186-1.ch011" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3693-8186-1.ch011</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007273514?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007273514?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 Malhotra S., Christ University, India; Dhanya K.A., Christ University, India; Prathibha R.M.S., Christ University, India; Mohanty P., Christ University, India Description An account of the resource This study examines the application of AI frameworks in real- world scenarios, including regulatory compliance, fraud detection, credit scoring, default prediction, and portfolio management (Moscato et al., 2020). By leveraging AI to gain deeper insights into borrower creditworthiness, financial institutions can enhance decision- making, streamline processes, and foster a more stable financial environment. However, alongside its transformative potential, the implementation of AI presents significant challenges. Ensuring data quality, improving model interpretability, adhering to regulatory compliance, and addressing ethical considerations are crucial to achieving fair and unbiased outcomes. This paper highlights both the opportunities and complexities associated with integrating AI into financial systems, emphasizing the need for balanced and responsible adoption. 2025 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-836938188-5; 979-836938186-1; Language A language of the resource English Type The nature or genre of the resource Book chapter 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