Intelligent intercompany automation: enhancing financial settlements with AI agents

Semlani, Deepesh Vinodkumar (2025) Intelligent intercompany automation: enhancing financial settlements with AI agents. International Journal of Science and Research Archive, 16 (1). pp. 2042-2050. ISSN 2582-8185

Intercompany financial processes are among the most complex and error-prone operations in global enterprises, often hindered by asynchronous data, manual reconciliation, and high compliance risks. This review explores the integration of AI agents and intelligent automation to enhance intercompany settlements. By analyzing state-of-the-art models, architectures, and real-world case studies, the paper identifies how intelligent agents embedded within ERP systems can automate transaction matching, anomaly detection, and exception handling. A theoretical model and practical implementation framework are introduced, supported by experimental results showing improvements in match rates, cycle time reduction, and auditability. This review also addresses key challenges such as explainability, data fragmentation, and governance. Future directions emphasize distributed intelligence, reinforcement learning, federated models, and regulatory-aligned AI, offering a roadmap for sustainable transformation in corporate finance operations.