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 Manjunatha, M.K.; Karimikonda, Harinadh; Dwivedi, Yogita; Komatiguntala, Dhanalakshmi Title A name given to the resource Corporate Credit Rating Assessment for Financial Risk and Regulatory Compliance 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 Proceedings of the 4th IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2026; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IATMSI68868.2026.11465453" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IATMSI68868.2026.11465453</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105036965879?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105036965879?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 Manjunatha M.K., Visvesvaraya Technological University, Centre for PostGraduate Studies, Department of Management Studies, Mysuru, India; Karimikonda H., School of Management Studies (MBA), Sreenidhi Institute of Science and Technology, Telangana, Hyderabad, India; Dwivedi Y., Jaypee Business School, Jaypee Institute of Information Technology, Noida, India; Komatiguntala D., School of Business and Management, Christ University, Banglore, India Description An account of the resource Accurate corporate credit rating is crucial to financial risk management and regulation but the current models tend to use narrow data modalities, fail to consider time and relational relationships and have weak probabilistic calibration. These constraints make them less effective in detecting the risk of default and under pinning decision-making that is in line with the regulator. The objective of this study was to formulate and test a multimodal model with a time-dependent credit rating system to incorporate financial, textual, market and relational information. The publicly available corporate financial statements, market time series data, text disclosures and inter-firm relational information were used to conduct an experimental study. Baseline logistic regression, a hybrid XGBoost with FinBERT embeddings model, and a proposed Temporal Heterogeneous Graph Transformer with cross-modal fusion were implemented and compared using discrimination, calibration, and computational efficiency metrics. The model proposed had the best predictive performance up to a ROC-AUC of 0.903 and PR-AUC of 0.482 which is better than the baseline (0.761) and hybrid (0.842) models. Calibration analysis revealed more correspondence with observed default frequencies, and confusion matrices revealed that the number of true default detection improved as 64 (baseline) to 158. Ablation and Pareto analysis was used to verify that multimodal fusion and temporal graph modelling were the major sources of performance improvements. These findings indicate that the combination of multimodal, temporal, and relational data has a significant positive effect on the accuracy and reliability of credit ratings and provides an institutional and supervisory-appropriate credit risk evaluation framework to the regulator. 2026 IEEE. Subject The topic of the resource Corporate credit rating; Credit risk modelling; Financial risk assessment; Multimodal learning; Regulatory compliance; Temporal graph transformer Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833154970-1; 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