AI-driven transformation in general ledger systems: A technical deep dive

Jha, Rakesh Kumar (2025) AI-driven transformation in general ledger systems: A technical deep dive. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 2363-2371. ISSN 2582-8266

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Abstract

Integrating Artificial Intelligence into General Ledger (GL) systems marks a revolutionary transformation in financial operations, particularly within the insurance sector. This technological advancement enables real-time transaction processing, automated reconciliation, and enhanced anomaly detection capabilities. Implementing AI-driven systems has significantly improved operational efficiency, reduced manual intervention, and strengthened regulatory compliance. Through sophisticated machine learning pipelines, advanced data processing architectures, and intelligent automation, these systems provide enhanced decision-making capabilities while maintaining data integrity and security. The evolution of GL systems continues to be shaped by emerging technologies such as blockchain, natural language processing, and edge computing, promising further improvements in financial operations management and risk assessment. The transformation encompasses comprehensive risk management strategies, predictive analytics capabilities, and automated compliance monitoring, creating a robust framework for financial institutions to navigate complex regulatory environments while optimizing operational performance. These advancements represent a fundamental shift in how financial institutions manage and process transactions, setting new standards for accuracy, efficiency, and security in financial operations.

Item Type: Article
Official URL: https://doi.org/10.30574/wjaets.2025.15.3.1132
Uncontrolled Keywords: Artificial Intelligence Integration; Financial Technology Innovation; Automated Reconciliation Systems; Regulatory Compliance Automation; Machine Learning Applications
Depositing User: Editor Engineering Section
Date Deposited: 22 Aug 2025 07:15
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URI: https://eprint.scholarsrepository.com/id/eprint/4987