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 Poonia, Ramesh Chandra; Prabu, P.; Maheshwari, Abhishek; Malhotra, Amit; Gupta, Varuna Title A name given to the resource AI Enhanced Global Economic Resilience: Predicting and Mitigating Financial Crises 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 Lecture Notes in Networks and Systems;Volume;1122;pp.267-279 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7426-5_20" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7426-5_20</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218183138?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218183138?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 Poonia R.C., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India; Prabu P., CHRIST (Deemed to be University), Bangalore, 560029, India; Maheshwari A., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India; Malhotra A., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India; Gupta V., CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, 201003, India Description An account of the resource Global economic resilience relies on our ability to predict and mitigate financial crises, especially for small and medium-sized enterprises (SMEs)vital drivers of economic growth. These SMEs are particularly susceptible to market fluctuations in business-to-business or consumer-focused sectors. Organizations integration of big data technologies has revolutionized global financial data management, enhancing our resilience. In our interconnected world, the timely identification of impending financial crises is crucial. It's the linchpin to prevent catastrophic collapses that could send shockwaves through the global economy and societies. To address this challenge, we introduce the Nature-inspired Red-optimized Stochastic Artificial Neural Network (NRFO-SANN), a powerful instrument for detecting global financial crises and anomalies. Our approach leverages a diverse array of financial data collected worldwide. Employing Minmax normalization, we meticulously pre-process the data, ensuring its readiness for analysis. Principal Component Analysis (PCA) extracts the core features crucial for crisis identification. These insights fuel the implementation of the NRFO-SANN method, unlocking the potential of AI-driven prediction. The results are remarkable. Our NRFO-SANN model not only outperforms its peers but does so resoundingly. With an impressive 96% accuracy rate, it operates efficiently, taking just 1s for computations. It boasts an F-score of 96.5%, a sensitivity of 94% and a specificity of 95%. This model equips us with a robust tool for anticipating and responding to global financial crises, ultimately reinforcing the stability and resilience of the global economy and societies. In this era of AI-empowered global economic resilience, we possess enhanced capabilities to navigate the intricacies of our interconnected world. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Artificial intelligence; Computational time; Financial crisis prediction; Globalization; Principal component analysis; Stochastic artificial neural network Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981977425-8; 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