AI-Driven Cloud Enterprise Decision Platform for Predictive Fraud Detection and Secure Biometric Authentication across Networks
DOI:
https://doi.org/10.15680/jdm9pe59Keywords:
AI-driven enterprise systems, cloud security, predictive fraud detection, biometric authentication, enterprise decision platforms, zero-trust architecture, network security, behavioral analytics, cybersecurity analytics, compliance-driven intelligenceAbstract
The rapid digitalization of enterprise ecosystems has significantly increased exposure to cyber fraud, identity theft, and unauthorized network access. Traditional rule-based security mechanisms are insufficient to detect sophisticated fraud patterns across distributed cloud and mobile environments. This paper proposes an AI-driven cloud enterprise decision platform that integrates predictive fraud analytics, secure biometric authentication, and cross-network intelligence to enhance organizational security and decision-making. The proposed architecture leverages machine learning, deep learning, and behavioral analytics within a scalable cloud infrastructure to identify anomalies, prevent financial fraud, and ensure secure identity verification across enterprise systems.
The framework combines biometric modalities such as facial recognition, fingerprint scanning, and behavioral biometrics with real-time fraud detection engines. Data from enterprise resource planning (ERP) systems, mobile devices, financial applications, and network logs are aggregated into a centralized cloud data platform. AI models perform predictive risk scoring, anomaly detection, and contextual decision analysis. A zero-trust network model and encryption protocols ensure secure data transmission and regulatory compliance. The system incorporates explainable AI (XAI) and compliance-driven governance to support transparency, auditability, and ethical decision automation.
Experimental simulations demonstrate improved fraud detection accuracy, reduced false positives, and enhanced authentication reliability compared with conventional enterprise security systems. The results highlight the platform’s effectiveness in enabling proactive threat detection, secure identity verification, and real-time decision intelligence. The proposed approach provides a scalable and compliance-aware solution for enterprises operating across cloud, mobile, and networked environments.
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