Explainable Cloud-Native Fraud Detection with Generative AI
Keywords:
Hybrid Cloud Native Architecture, Insurance Fraud Detection, Explainable Artificial Intelligence, XAI For Fraud Models, Machine Learning Life Cycle Management, MLOps And AIOps Integration, Telecommunications Data Analytics, Proprietary Enterprise Data, Supervised Classification Models, Fraud Risk Scoring, Model Explainability And Transparency, Regulatory Compliance In AI, Class Imbalance Calibration, Business Decision Support Systems, Hybrid Cloud Deployment Models, Operational Fraud Monitoring, AI Governance And Trust, Generative AI For Explainability, Enterprise Scale ML Systems, Risk Mitigation Frameworks.Abstract
This study addresses the explainability challenges of insurance fraud detection models built on proprietary telecommunications data, using a hybrid cloud-native architecture combined with machine learning (ML) and AIOps technologies. Supervised classification models for fraud detection are powered by a combination of internal and external enterprise data alongside historical fraud incident records. Explainable Artificial Intelligence (XAI) techniques, which clarify how models arrive at their decisions and outcomes, are essential for securing business acceptance, meeting regulatory requirements, and aligning with risk-mitigation goals. By moving beyond accuracy-centric objectives toward a more holistic evaluation framework, this research addresses a critical gap in the literature.
The findings show that integrating telecommunications data with historical fraud event records improves model accuracy, while also underscoring the importance of calibrating for class imbalance. A realistic simulation of a high-tech anti-fraud operation demonstrates how a hybrid cloud-native architecture with MLOps capabilities can support the entire ML lifecycle. With proper scrutiny and evaluation, these models can inform business decision-making and be deployed within AIOps workflows built on hybrid cloud infrastructure. Looking forward, this work identifies two promising directions for future research: enhancing the explainability of fraud detection models through generative AI, and establishing new methodological standards for deploying explainable fraud detection models in hybrid cloud environments.
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