Integrating AI with Oracle Fusion ERP for Autonomous Financial Close
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P109Keywords:
Artificial Intelligence, ERP, Oracle Fusion, Financial Close, Machine Learning, Automation, ReconciliationAbstract
Financial close is a crucial but time-consuming part of enterprise financial management, which has long been associated with tedious manual journal entries, an inaccurate reconciliation process, and a resource-consuming audit trail. This paper researches how Artificial Intelligence (AI) and Machine Learning (ML) can be implemented in the Oracle Fusion Cloud ERP to facilitate an autonomous financial close. Oracle Fusion is an automation application that can be based on embedded intelligence and a rules engine to automate major functions, including creating journal entries, intercompany reconciliations, and provisions related to detecting anomalies in financial statements. This paper discusses the system design, automation cues, and data pipeline processes in supporting the AI-based financial package at Oracle. In a process-oriented evaluation of the performance of Oracle Intelligent Financials Model compared to a conventional manual process, we examine how Oracle Intelligent Financials functions. Our empirical evidence suggests that with AI-powered journal automation, predictive reconciliations can reduce close-cycle length by 40 percent, increase compliance accuracy, and enhance audit transparency. In our examination, we also reveal the weaknesses of existing rule-based models, particularly in situations with high variability or non-standard transactions. This paper concludes by highlighting the potential impact of CFOs, auditors, and enterprise IT teams as autonomous financial close systems become intelligent and self-correcting in the contemporary digital enterprise
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