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 Siby, Amala; Jha, Babita Title A name given to the resource Managing Retail Credit Default Risk Using Artificial Intelligence and Machine Learning: Ethical Considerations and Challenges 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 Lecture Notes in Networks and Systems;Volume;1509 LNNS;pp.351-359 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-8793-0_28" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-8793-0_28</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028314421?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028314421?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 Siby A., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Jha B., School of Business and Management, Christ University, Bengaluru, India Description An account of the resource Credit risk management is an integral aspect of financial stability of financial institutions. The determinants of credit default behavior help in identifying types of credit risk based upon customers habits or actions such as purchasing behavior, spending patterns, social interactions, and moral hazard. The awareness on such behavioral factors helps in getting a complete picture in analyzing the credit worthiness of any individual. The onset of artificial intelligence (AI) and machine learning (ML) has remodeled the methodologies of credit risk assessment. Training and developing learning models to predict the borrowers behavior evolved along with AI and ML, developing advanced mechanisms for managing credit default risks. Using risk modeling to determine behavioral scoring enables the banks to acquire creditworthy retail borrowers, alongside its inherent demerits of the models. This study is focused on the employment of AI and ML techniques along with qualitative determinants in managing credit risk, drawing attention to the advantages and disadvantages and applications and implications for the financial service providers. The paper examines contemporary literature on modern methods of credit risk management and sheds light on the technological accuracy of credit risk calculations and automated decision-making processes alongside methods to alleviate biases. The study also outlines the sequential phases of the conventional models along with the regulatory and ethical considerations of AI-ML-driven credit risk assessment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence; Credit risk assessment models; Data-driven decision models; Ethical considerations in credit default models; Machine learning; Retail credit Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981968792-3; 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