Explainable GenAI Fraud Defense in Hybrid Cloud Insurance Systems
DOI:
https://doi.org/10.5281/zenodo.20570452Keywords:
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
Using hybrid cloud-native architecture, machine learning (ML) and AIOps technologies, this study addresses the explainability challenges of insurance fraud identification models built on proprietary telecommunications data. Internal and external enterprise data, together with historical fraud incident records, power supervised classification models for fraud detection. Explainable Artificial Intelligence (XAI) technologies, which shed light on model decision-making processes and outcomes, are essential for business acceptance, regulatory compliance, and alignment with risk-mitigation objectives. The research thus fills a critical gap in the literature, encouraging organizations to move beyond accuracy-centric goals and embrace a more comprehensive view.
The findings reveal that combining data from telecommunication systems with historical fraud event data leads to more accurate models and highlights the importance of class-imbalance calibration. A realistic simulation involving a high-tech anti-fraud operation showcases the potential of a hybrid cloud-native architecture with MLOps capabilities for supporting the whole ML life cycle. With appropriate scrutiny and evaluation, these models can serve business decision-making and be deployed within AIOps procedures built on hybrid cloud architectures. Looking ahead, the investigation points to fruitful future work in two areas: enhancing the explainable capabilities of fraud models through generative AI and establishing new methodological standards for the deployment of explainable fraud detection models within hybrid cloud environments.
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