AI-Driven Anomaly Detection In Peoplesoft Financial Systems

Abstract

Enterprise Resource Planning (ERP) products like Oracle PeopleSoft have customarily been the transactional systems of record within large enterprises with millions of financial transactions flowing through ERP general ledgers, accounts payable, procurement, and expense management modules. Controls and monitoring of financial transactions within ERP systems have customarily been rules-based, with transactional data run through rule sets to check against pre-defined limits. Although useful, these mechanisms are backward-looking and context-blind and are gradually failing to keep up in the face of high-level fraud, explosive data growth, and ever-increasing regulatory demands. This article focuses on artificial intelligence (AI) use in ERP financial management, concentrating on machine learning-based anomaly detection. Architecture, technology, organization, and market perspectives on this transformation are discussed, with Oracle PeopleSoft as a case study for enterprise application suites. Topics include unsupervised learning algorithms and temporal neural networks. Engineering the architecture of ERP data structures for real-time integration, ensuring explainability for audit requirements, and addressing market drivers for AI integrations within legacy financial environments. The conclusion of the article is that such AI-based anomaly detection goes beyond a technological shift and is a fundamental reorganization of the way financial trust, governance, and oversight are conducted in large organizations. Firms that resist this shift will increasingly face governance risk.