AI-Enabled SAP Secure Financial Operations for Credit Risk and Fraud Detection with Regulatory Compliance and Smart City Network Benchmarking
DOI:
https://doi.org/10.15662/IJARCST.2024.0706029Keywords:
Artificial Intelligence, SAP Financial Operations, Credit Risk Assessment, Fraud Detection, Regulatory Compliance, Smart Cities, Network Benchmarking, Machine Learning, Financial Security, ERP SystemsAbstract
The rapid digitalization of financial services within smart city ecosystems has significantly increased the complexity of credit risk assessment, fraud detection, and regulatory compliance. Enterprise Resource Planning (ERP) platforms such as SAP play a pivotal role in managing large-scale financial operations, yet traditional rule-based systems struggle to address evolving fraud patterns, real-time risk evaluation, and cross-regulatory requirements. This paper proposes an AI-enabled SAP secure financial operations framework that integrates machine learning–based credit risk modeling, intelligent fraud detection, regulatory compliance automation, and smart city network benchmarking. The framework leverages advanced analytics, SAP HANA in-memory computing, and AI-driven governance mechanisms to enhance financial security, transparency, and resilience. Experimental results demonstrate improved fraud detection accuracy, reduced credit default risk, enhanced regulatory adherence, and scalable benchmarking across smart city financial networks. The findings highlight the transformative role of AI-driven SAP systems in enabling secure, compliant, and data-driven financial operations in smart city environments.References
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