Title: Artificial Intelligence Across Disciplines: Research, Innovation, and Intelligent Solutions
Editors: Dr. Subita Bhagat, Dr. A. Balamurugan, Dr. P. Krishna Kumar, and Mrs. S. Nandhini Devi
ISBN: 978-81-69857-83-3
Chapter: 18
DOI: https://doi.org/10.59646/815/18
Author: Dr. T. J. Arun
Abstract
Artificial intelligence (AI) has rapidly transitioned from an emerging experimental paradigm into an indispensable pillar of modern corporate accounting, assurance, and financial reporting ecosystems. This chapter examines the deployment, structural mechanisms, and systemic outcomes of computational intelligence frameworks designed to modernize enterprise accounting ledgers, automate substantive audit workflows, and accelerate compliant financial disclosure generation. Leveraging an empirical case study of a multinational enterprise conglomerate processing over five million general ledger journals and multi-tier consolidation entries, we implement and evaluate a tripartite analytical architecture: an automated machine-learning reconciliation pipeline, an unsupervised autoencoder coupled with Isolation Forests for journal-entry anomaly detection, and a fine-tuned Transformer-based Natural Language Processing (NLP) architecture for continuous disclosure tagging and regulatory reporting verification. Our findings indicate that machine intelligence reduces manual reconciliation cycle times by 84.6%, identifies fraudulent or aberrant general ledger postings with an F1-score of 0.946, and slashes material disclosure discrepancies. Ultimately, this chapter establishes a comprehensive operational foundation for implementing robust, scalable, and audit-compliant AI solutions across global reporting frameworks.
Keywords: Artificial Intelligence, Continuous Auditing, Anomaly Detection, Machine Learning Reconciliation, Financial Reporting Automation, Natural Language Processing.