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 Lakshmanjani, Pillada Naga; Rangasamy, Sangeetha Title A name given to the resource Role of AI in Computational Risk Modeling of Financial Stability and Portfolio Risk: A New Perspective 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 Computational Modelling Approaches to FinTech Innovation;pp.139-161 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.2174/9798898810818125010010" target="_blank" rel="noreferrer noopener">https://doi.org/10.2174/9798898810818125010010</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025274368?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025274368?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 Lakshmanjani P.N., School of Business and Management, CHRIST, Deemed University, Karnataka, Bengaluru, 560029, India; Rangasamy S., School of Business and Management, CHRIST, Deemed University, Karnataka, Bengaluru, 560029, India Description An account of the resource The need to assess climate change-related risks and their impact on the financial stability of banks is imperative. Innovations in technology, especially AI andML algorithms, have improved the efficiency and accuracy of risk analysis models. The obstacle for banks is assessing the climate risk exposure due to their lending portfolio. The climate data are uncertain and unavailable, and the granularity of the data is questionable. To overcome these issues, in this chapter, a hybrid risk predictive model is proposed. It uses a combination of ResNet-50 (to analyze and quantify spatial image data) and CoViaR (risk prediction) models. Using the ResNet-50 model, a climate change risk score is developed from images and feature extraction, which is correlated with the emission volume of the borrower firms. Then, using the proposed model, the impact of climate change-related risk on the lending portfolio is evaluated to understand the financial stability of banks through capital. 2025, Bentham Books imprint. Subject The topic of the resource AI predictive risk modeling; Artificial intelligence; Capital reserves; Climate change risk score; Climate data; CoViaR; Hybrid model; Lending portfolio; Portfolio risk; Risk analysis model; Spatial image data; Temperature maps; VaR Publisher An entity responsible for making the resource available Bentham Science Publishers Relation A related resource ISBN: 979-889881081-8; 979-889881083-2; 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