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 Article Faculty Publications -Articles 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 Uppal, Aakanksha; Srivastava, Anubha; Awasthi, Yashmita; Srivastava, Anjita; Kakkar, Barkha Title A name given to the resource Translating artificial intelligence into socio-economic insight: a hybrid deep learning approach to employee financial well-being 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 Discover Artificial Intelligence;Volume;6;Issue;1;Article No.;248; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s44163-026-00949-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s44163-026-00949-2</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105034192001?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034192001?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 Uppal A., Symbiosis International (Deemed University) Pune, Symbiosis Law School Noida Campus, Noida, Ghaziabad, India; Srivastava A., Institute of Technology &amp; Science, Mohan Nagar, Ghaziabad, India; Awasthi Y., School of Commerce, Finance and Accountancy, Christ University, Bengaluru, India; Srivastava A., Bundelkhand University, Jhansi, India; Kakkar B., Institute of Technology &amp; Science, Mohan Nagar, Ghaziabad, India Description An account of the resource This study aims to translate recent advancements in hybrid artificial intelligence (AI) modeling into a functional tool for assessing individual financial well-being. The objective is to develop a system that aids organizations in understanding employees financial stress, with broader implications for enhancing workplace productivity and societal economic resilience. A deep learning pipeline was developed to classify individuals into three financial well-being categories: Financially Secure, Moderately Stable, and Financially At-Risk. The approach utilizes a structured dataset of 20,000 Indian individuals and implements 15 advanced deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Wide &amp; Deep networks. Model performance was assessed using standard evaluation metrics, including validation accuracy and ROC-AUC scores. Among the tested models, the hybrid Wide &amp; Deep + CNN configuration yielded the highest performance, achieving a validation accuracy of 99.44% and a perfect ROC-AUC score of 1.0000. These results validate the models capacity for robust classification and real-world applicability to financial profiling. This study demonstrates a practical application of AI in financial decision support systems and contributes to organizational research by offering a scalable solution to assess and mitigate employee financial stress. The Author(s) 2026. Subject The topic of the resource CNN; Deep learning; Employee financial stress; Financial well-being assessment; Organizational productivity; Societal economic stability; Wide &amp; deep network Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 27310809; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online