Intelligent SAP Workforce Scheduling: AI/ML-Driven Productivity, Anomaly Detection, and Compliance in Digital Banking with Oracle Integration
DOI:
https://doi.org/10.15662/IJRAI.2024.0705003Keywords:
Artificial Intelligence, Machine Learning, SAP, Workforce scheduling, Digital banking, Oracle integration, Productivity optimization, Anomaly detection, Compliance management, Predictive analytics, Reinforcement learning, Financial technology, Operational efficiency, Regulatory complianceAbstract
The digital transformation of the banking sector demands intelligent workforce management solutions that ensure operational efficiency, compliance, and adaptability. This paper introduces an AI/ML-driven workforce scheduling framework within SAP, integrated with Oracle-based digital banking systems, to optimize productivity, detect anomalies, and ensure regulatory compliance. The proposed model leverages predictive analytics, reinforcement learning, and dynamic optimization algorithms to forecast workload demands, allocate human resources efficiently, and adapt schedules in real time. Anomaly detection mechanisms continuously monitor workforce behaviors, task completion rates, and system interactions to identify irregularities or potential compliance breaches. Through secure SAP–Oracle integration, the framework supports unified data visibility, audit transparency, and seamless inter-system communication. The study’s findings reveal that intelligent workforce scheduling reduces idle time, enhances employee performance, and minimizes compliance risks in high-security financial environments. This research underscores how AI and ML, when embedded in SAP and harmonized with Oracle infrastructure, create resilient, efficient, and regulation-aligned digital banking ecosystems.
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