Anomaly Detection in Expense Management using Oracle AI Services
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P109Keywords:
Expense Management, Anomaly Detection, Oracle AI Services, Machine Learning, ERP Fraud Detection, Oracle Fusion ERP, Cloud-Based AnalyticsAbstract
Enterprise Resource Planning (ERP) systems must have expense management systems that avert financial leakage and avoid violation of organizational policy. The detection models, based on the rules, are ineffective in identifying complex and new fraudulent behaviors and can result in an increased financial risk. Oracle Fusion ERP enables the integration of Machine Learning (ML)-based anomaly detection using Oracle AI Services, providing a powerful platform for anomaly detection. The current paper proposes a comprehensive model of anomaly detection of expense reports using ML algorithms trained using historical transactions data in Oracle Fusion ERP. The suggested model combines both supervised and unsupervised models, including Isolation Forest, Autoencoders, and Oracle Adaptive Intelligence (AI) services, which it uses to flag suspect transactions. Contextual data (e.g. vendor behavior, employee trends in expenses, seasonal changes, etc.) is also exploited within the framework to further increase the accuracy of detection. We cover the design of Oracle AI Services, data pipelines that serve real-time inference, and how to consume them with Oracle Cloud Infrastructure (OCI). Synthetic and semi-realistic test data on enterprise expense logs prior to 2022 demonstrated that the approach significantly outperforms classical approaches based on thresholds in terms of anomaly detection rate. The findings suggest that the increase in fraud detection accuracy by up to 35%, the reduction in false positives by 28%, and the efficiency of auditing by 40% are observed when determined by ML-based anomaly detection. Additionally, the research highlights issues with explainability, data governance, and the real-time performance of inferences. The paper recommends future integration of AI-driven detection with predictive analytics in an ERP in its conclusion
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