Detecting and Preventing Fraud in Oracle Cloud ERP Financials with Machine Learning
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P107Keywords:
Oracle Cloud ERP, Machine Learning, General Ledger (GL), Accounts Payable (AP), Accounts Receivable (AR), Anomaly DetectionAbstract
The more complex and large the enterprise financial system becomes, the greater the opportunities to commit fraudulent activities. The Oracle Cloud ERP Financials, which is a very popular solution, is used to control key financial processes that include General Ledger (GL), Accounts Payable (AP), and Accounts Receivable (AR). Rule-based traditional fraud detection mechanisms are often unable to cope with changing forms of fraud, failing to identify subtle anomalies, or creating too many false positives. In the current paper, we are introducing a machine learning-based system of continuous fraud monitoring in Oracle Cloud ERP, where supervised and unsupervised machine learning can be used to spot suspicious behavior in real-time. The outlined system can integrate strongly with Oracle Fusion applications, as it will rely on data preprocessing, feature engineering, and advanced models in the cycle of random forests, isolation forests, and time-series anomaly detection. It tracks the pattern of transactions, user activities, and audit trail to identify anomalies in various financial modules. Case studies on synthetic ERP logs and real financial data demonstrate promising results, where accuracy and recall of more than 90 % as well as the mean detection latency, are recorded at less than two seconds. This strategy helps to eliminate much manual control, enhance detection accuracy, and provide a means of action by directly inserting intelligent fraud detection into the ERP environment. The findings show that financial control in the contemporary system of enterprises can be strengthened, and the risk exposure level can be reduced with the help of AI-based solutions
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