Leveraging Machine Learning for Anomaly Detection in Oracle Financial Consolidation and Close Cloud Service (FCCS)

Authors

  • Ramsundernag Changalva

Keywords:

Anomaly Detection, Machine Learning, Financial Consolidation, Oracle FCCS, Audit Automation, Risk Management

Abstract

In the realm of financial consolidation, ensuring data integrity and compliance is paramount. Traditional methods of anomaly detection often fall short in identifying subtle irregularities within vast datasets. This paper explores the integration of Machine Learning (ML) techniques into Oracle's Financial Consolidation and Close Cloud Service (FCCS) to enhance the detection of anomalies such as unusual variances and accounting errors. By leveraging ML algorithms, we propose a framework that proactively identifies potential risks in financial consolidation processes, thereby augmenting automated audit trails and ensuring robust financial oversight.

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Published

06.08.2024

How to Cite

Ramsundernag Changalva. (2024). Leveraging Machine Learning for Anomaly Detection in Oracle Financial Consolidation and Close Cloud Service (FCCS). International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 2186–2198. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7304

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

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