The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Algorithmic Modeling of ESG Contingencies Using Intelligent Analytics and Predictive Parameterization of Corporate Risk
Abstract
This study presents a hybrid intelligent analytics architecture that merges low-orbit satellite data, IoT sensors, multilingual digital narratives, and extended financial statements using interoperable ontologies to model ESG contingencies and parameterize corporate risk. Deep neural networks are combined with hierarchical Bayesian graphs and Shapley explainability metrics, allowing environmental, social, and reputational incidents to be anticipated with greater sensitivity than traditional approaches. The responsible algorithmic governance framework, underpinned by ethical licensing and continuous audits, ensures transparency and multi-group fairness, balancing intellectual protection and accountability. Case studies in the energy, financial, and logistics sectors show substantial reductions in prediction errors, capital buffers, and stock market drawdowns, while strengthening supply chain resilience and management acceptance.
Related Content
|
Marcos Komodromos, Sofia Anastasiadou, Lamprini Seremeti.
© 2026.
36 pages.
|
|
Lamprini Seremeti, Lazaros Anastasiadis, Marcos Komodromos.
© 2026.
12 pages.
|
|
Eleftheria Panagiotidou, Elena Kagioglou, Athanasios Mandilas, Giannoula Florou.
© 2026.
38 pages.
|
|
José G. Vargas-Hernandez, Csongor Czipf, Absalón J. Salmerón-Zapata.
© 2026.
28 pages.
|
|
José G. Vargas-Hernandez, Csongor Czipf, Absalòn J. Salmeròn-Zapata.
© 2026.
26 pages.
|
|
Jose De Jesus Reyes-Sánchez.
© 2026.
32 pages.
|
|
K. Balaji.
© 2026.
26 pages.
|
|
|