International Journal of Science, Engineering and TechnologyAuthors: Pallavi Droniya
Abstract: Effective credit management is essential for maintaining financial stability and operational efficiency in modern enterprises. Traditional methods, relying on manual assessments and historical financial data, are often reactive and insufficient for predicting credit risk in dynamic market environments. This paper explores the integration of predictive analytics and deep learning techniques into SAP systems to enable next-generation credit intelligence. By leveraging historical and real-time data, advanced models such as neural networks and LSTM capture complex patterns in customer behavior, allowing proactive risk assessment, dynamic credit limit optimization, and early warning of potential defaults. The paper discusses SAP integration workflows, performance evaluation metrics, and emerging trends, including AI, cloud computing, and real-time analytics, that are shaping the future of enterprise credit management. The findings highlight that combining ERP platforms with predictive modeling not only improves operational efficiency and regulatory compliance but also facilitates data-driven strategic decision-making, positioning organizations for sustainable financial growth.
DOI: https://doi.org/10.5281/zenodo.18161634
