Cross-enterprise dataset modeling for scalable AI in Workday ERP environments: a multi-domain benchmark study

Abstract

Enterprise resource planning (ERP) systems that are based on workdays are becoming dependent on extensive, heterogeneous and rapidly changing financial information, but current tools are constrained by restricted data structures, hand written rules, and poor generalization among enterprises. When implemented in the context of organizations of varying transaction volumes, document structures, and risk indicators, these restrictions pose serious impediments to the automation of major financial processes, namely, wire transfers, payment to employees and budget approvals. To fill this gap, a scalable cross-enterprise data modelling is suggested that can learn unified features using heterogeneous financial logs and enhance adaptive automation when applying Workday ERP processes. The proposed methodology utilizes a differentiable deep neural network (DDNN) predictive model, anomaly detection and workflow risk scoring; and a modified dung beetle optimization (MDBO) algorithm is used to optimize the routing of decisions, reduce processing delays and improve the selection of multi-domain features. An inter-enterprise benchmark dataset is built by aligning structured and unstructured financial data with the help of ontology-based normalization, and the DDNN-MDBO model is tested using two example ERP processes inter-bank wire transfer and employee reimbursement cycles. The comparison of two workflows indicates that the DDNN of 97.1% and MDBO model have 98.1% and 96.2% accuracy on inter-bank transfers and reimbursements, respectively, with the precision, recall and ROC AUC scores being also high and decreasing false positives/negatives by 42%. MDBO increases risk control and parallelization by 100 percent and cuts the processing time by 28% shortens turnaround time of cross enterprise ERP automation is robust and scalable, and predictive performance and operational efficiencies are improved.