International Journal of Science, Engineering and TechnologyAuthors: Aadhya Mittal
Abstract: In an era of unprecedented market volatility and high-frequency digital transactions, traditional retrospective financial risk management has become insufficient for modern enterprise governance. This review article investigates the design and implementation of intelligent risk assessment systems that integrate advanced Machine Learning (ML) techniques with SAP ERP analytics. By utilizing the unified transactional foundation of SAP S/4HANA and the agile innovation capabilities of the SAP Business Technology Platform (BTP), organizations can transition from reactive auditing to proactive, real-time risk mitigation. The article explores a multi-layered modeling approach, including supervised ensembles for credit scoring, unsupervised anomaly detection for fraud identification, and deep learning architectures like Long Short-Term Memory (LSTM) networks for liquidity forecasting. A significant focus is placed on the technical architecture required to bridge the "sim-to-real" gap, the role of SAP HANA’s in-memory computing in enabling sub-second risk inference, and the integration of Explainable AI (XAI) to meet stringent global regulatory standards. Furthermore, we address strategic barriers such as data hygiene, algorithmic bias, and the talent gap, while forecasting the impact of agentic AI and federated learning on the future of corporate finance. The findings provide a comprehensive framework for CFOs and system architects to build a resilient, "intelligence-first" financial ecosystem capable of navigating the complexities of the 2025 global economy.
