Designing Secure Digital Payment and Revenue Attribution Systems using AI and Cloud Security Frameworks
DOI:
https://doi.org/10.15662/jpdbnt31Keywords:
Digital payment security, Artificial Intelligence (AI), Cloud security frameworks, Revenue attribution systems, Fraud detection, Zero Trust architecture, Machine learning, Secure APIs, Tokenization, Blockchain verificationAbstract
The rapid proliferation of digital payment platforms has revolutionized global commerce, fostering convenience, inclusivity, and economic efficiency. Simultaneously, this growth has escalated security threats and revenue attribution challenges — fraud, identity theft, unauthorized access, and ambiguous transaction verification — threatening financial stability and consumer trust. This research explores the integration of Artificial Intelligence (AI) and cloud security frameworks to design secure digital payment infrastructures and robust revenue attribution systems. A hybrid model leveraging machine learning (ML), predictive analytics, anomaly detection, and cloud-native defenses is proposed to ensure real-time authentication, fraud prevention, data protection, and precise revenue tracking. The framework incorporates Zero Trust security, multi-factor authentication, blockchain verification, secure APIs, and secure cloud storage mechanisms, ensuring compliance with international regulations and optimizing system performance. Leveraging advancements in AI-driven fraud detection and cloud security architectures, the proposed system enhances threat detection accuracy, reduces false positives, and strengthens trust in financial ecosystems. Empirical evaluation highlights significant improvements in transaction integrity, scalability, and threat resilience. Key implementation challenges — such as ethical AI deployment, data privacy concerns, and service latency — are also discussed. This study advances secure transaction frameworks and provides a scalable roadmap for future digital financial systems. IJSRCSEIT
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