A Cloud-Secure Explainable Intelligence Model for Multi-Source Fraud Detection AI-Driven Threat Analytics and Big Data Engineering

Abstract

The rapid growth of digital financial ecosystems has intensified the need for secure, transparent, and scalable fraud detection systems capable of analyzing massive and diverse data streams. This paper proposes a cloud-secure explainable intelligence model that integrates multi-source data fusion, big data engineering, and AI-driven threat analytics to detect sophisticated fraud patterns in real time. The framework leverages distributed data pipelines, advanced feature engineering, and parallelized processing to manage heterogeneous datasets, including transaction logs, behavioral signals, system events, and identity metadata. A hybrid deep learning architecture combining supervised and unsupervised models enhances anomaly detection accuracy, while explainable AI (XAI) methods—such as SHAP and LIME—ensure transparency, interpretability, and regulatory compliance across detection workflows. To strengthen resilience, the system incorporates privacy-preserving analytics, including differential privacy and secure multi-party computation, enabling institutions to collaborate without exposing sensitive data. Cloud-native security controls—identity management, encryption, access governance, and continuous monitoring—provide a robust defense against emerging cyber threats. Experimental evaluations demonstrate improved detection performance, reduced false positives, and enhanced analyst trust through interpretable insights. The proposed model offers a scalable, secure, and explainable approach suitable for financial institutions, e-commerce platforms, and cybersecurity operations centers.