ItemArticle

Translating artificial intelligence into socio-economic insight: a hybrid deep learning approach to employee financial well-being

Title
Translating artificial intelligence into socio-economic insight: a hybrid deep learning approach to employee financial well-being
Creator
Uppal, Aakanksha; Srivastava, Anubha; Awasthi, Yashmita; Srivastava, Anjita; Kakkar, Barkha
Description
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 & Deep networks. Model performance was assessed using standard evaluation metrics, including validation accuracy and ROC-AUC scores. Among the tested models, the hybrid Wide & 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.
Source
Discover Artificial Intelligence;Volume;6;Issue;1;Article No.;248;
Date
01-01-2026
Publisher
Springer Nature
Subject
CNN; Deep learning; Employee financial stress; Financial well-being assessment; Organizational productivity; Societal economic stability; Wide & deep network
Coverage
Uppal A., Symbiosis International (Deemed University) Pune, Symbiosis Law School Noida Campus, Noida, Ghaziabad, India; Srivastava A., Institute of Technology & 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 & Science, Mohan Nagar, Ghaziabad, India
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 27310809;
Format
online
Language
English
Type
Article

Citation

Uppal, Aakanksha; Srivastava, Anubha; Awasthi, Yashmita; Srivastava, Anjita; Kakkar, Barkha, “Translating artificial intelligence into socio-economic insight: a hybrid deep learning approach to employee financial well-being,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22159.

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