Explainable Deep Learning for Intelligent Fraud Detection and Risk Analytics across Hybrid Cloud and Enterprise Ecosystems

Tsungai Tsambatare; Tsungai Tsambatare

doi:10.15662/ijetemr.2026.1105002

Explainable Deep Learning for Intelligent Fraud Detection and Risk Analytics across Hybrid Cloud and Enterprise Ecosystems

Tsungai Tsambatare

American University, Washington, United States

Abstract

The increasing sophistication of financial and enterprise fraud has created a need for intelligent detection systems capable of identifying complex, evolving, and previously unseen fraudulent behavior. Traditional rule-based approaches often struggle with high-dimensional transaction data, changing fraud patterns, and the scale of modern hybrid cloud environments. Deep learning provides powerful capabilities for discovering nonlinear relationships and subtle behavioral patterns, but its limited interpretability can create significant challenges for organizations operating in highly regulated and risk-sensitive domains. This paper proposes an Explainable Deep Learning framework for intelligent fraud detection and risk analytics across hybrid cloud and enterprise ecosystems. The proposed framework integrates deep neural networks, behavioral analytics, anomaly detection, graph-based relationship analysis, explainable artificial intelligence, real-time data processing, and risk scoring. A hybrid cloud architecture enables distributed processing while maintaining appropriate controls for sensitive enterprise and financial information. Explainability mechanisms are incorporated to provide human-understandable reasons for fraud predictions, identify influential transaction characteristics, and support auditability and investigation. The methodology combines data preprocessing, feature engineering, deep learning model development, explainability analysis, hybrid cloud deployment, and comparative experimental evaluation. Performance is assessed using fraud detection accuracy, precision, recall, F1-score, area under the precision-recall curve, false-positive rate, inference latency, computational cost, and explanation fidelity. The proposed approach aims to improve fraud detection effectiveness while maintaining transparency, operational scalability, regulatory accountability, and trust in automated risk analytics

Article Information

Journal	International Journal of Emerging Trends in Engineering and Management Research
Volume (Issue)	Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)
DOI	https://doi.org/10.15662/ijetemr.2026.1105002
Pages	21974-21985
Published	September 7, 2026
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Tsungai Tsambatare (%2026). Explainable Deep Learning for Intelligent Fraud Detection and Risk Analytics across Hybrid Cloud and Enterprise Ecosystems. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 5 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21974-21985. https://doi.org/10.15662/ijetemr.2026.1105002

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