Anomaly Detection in ERP Systems Using AI and Machine Learning

Abstract

ERP systems are critical in the administration of corporate activities and address huge volumes of transactional and operation information. However, since ERP systems combine many operations of an organization into one system it is prone to a possibility of developing an anomaly that can come from an erroneous data input or even instances of hacking hence causing operational insecurity and loss. This work seeks to understand how AI (Artificial Intelligence) and Machine Leaning (ML) can be used to determine abnormalities in ERP systems. Conventional methods of anomaly detection do not allow for detailed recognition and handling of complex patterns; thus, AI and ML are suitable for dynamic systems. The paper discusses different forms of anomalous situations in ERP systems and examines the potential of different learning techniques in increasing the effectiveness of anomaly identification. The framework that is presented provides for the incorporation of ML relied anomaly recognition in ERP systems to optimize operational efficiency as well as error identification in real time.