Mobile Edge AI Framework for Real-Time ESG Compliance Auditing through NLP and Blockchain Ledger Verification

Nur Alamzah; Mursalim Nohong; Hendragunawan Sardjan Thayf; Baginda Hamzah; Adel Abdulwadood Taher

doi:10.3991/ijim.v20i20.63187

Authors

Nur Alamzah Hasanuddin University, Makassar, Indonesia
Mursalim Nohong Hasanuddin University, Makassar, Indonesia
Hendragunawan Sardjan Thayf Hasanuddin University, Makassar, Indonesia
Baginda Hamzah Hasanuddin University, Makassar, Indonesia
Adel Abdulwadood Taher University of Baghdad, Baghdad, Iraq

DOI:

https://doi.org/10.3991/ijim.v20i20.63187

Keywords:

Mobile Edge AI, ESG Compliance, Natural Language Processing, Blockchain Verification, Real-Time Auditing

Abstract

The Mobile Edge artificial intelligence (AI) framework is introduced as an environmental, social, and governance (ESG) verification system that integrates natural language processing (NLP) and permissioned blockchain to facilitate near-real-time ESG compliance auditing. A lightweight transformer model is employed to identify ESG-related claims and extract relevant indicators, numerical values, units, locations, and reporting times. Extracted claims are structured and recorded on the blockchain to ensure verifiable, tamper-resistant records. A discrepancy assessment mechanism then classifies each claim as supported, partially supported, contradictory, or unsupported. The prototype was evaluated using a synthetic Kaggle dataset containing 11,000 annual company records from 1,000 organizations across nine regions and multiple sectors. The ESG classification model achieved a true positive rate of approximately 93.2%, with F1-scores of 0.945, 0.915, and 0.895 for environmental, social, and governance claims, respectively. Claim verification rates ranged from 93.1% to 98.4%. Edge deployment reduced verification latency to 124 ms compared to 231 ms in the cloud environment, representing a 46.3% reduction. These findings indicate that the framework can enable proactive ESG auditing, enhance claim traceability, accelerate verification, and deliver explainable compliance-risk signals.

Author Biographies

Nur Alamzah, Hasanuddin University, Makassar, Indonesia

Faculty of Economics and Business, Hasanuddin University, Makassar 90245, Indonesia

Mursalim Nohong, Hasanuddin University, Makassar, Indonesia

Faculty of Economics and Business, Hasanuddin University, Makassar 90245, Indonesia.

Hendragunawan Sardjan Thayf, Hasanuddin University, Makassar, Indonesia

Faculty of Economics and Business, Hasanuddin University, Makassar 90245, Indonesia.

Baginda Hamzah, Hasanuddin University, Makassar, Indonesia

Faculty of Economics and Business, Hasanuddin University, Makassar 90245, Indonesia.

Adel Abdulwadood Taher, University of Baghdad, Baghdad, Iraq

Department of Public Administration, College of Administration and Economics, University of Baghdad, Baghdad, Iraq.

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