Generative AI–Driven Cloud-Native Framework for Intelligent Insurance Fraud Detection and Analytics

  • Authors

    • Shashikala Valiki Independent Researcher, USA Author
    • Michael Thompson Student Research Assistant, Westlake University of Technology, United States. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2025PII7F3A

    Published 09-06-2025

  • Generative AI Fraud Analytics, Cloud-Native Insurance Systems, Insurance Fraud Detection, Machine Learning Fraud Models, Intelligent Fraud Prevention, Cross-Border Fraud Analytics, AI-Driven Anomaly Detection, Fraud Risk Intelligence, Cloud Insurance Platforms, Predictive Fraud Analytics

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    S. . Valiki and M. Thompson, “Generative AI–Driven Cloud-Native Framework for Intelligent Insurance Fraud Detection and Analytics”, IJETMR, vol. 8, no. 2, pp. 01–18, Sep. 2025, doi: 10.67228/30715636/IJETMR-2025PII7F3A.

  • Abstract

    This study investigates how generative artificial intelligence (AI) and machine learning (ML) techniques can enhance fraud detection and prevention in cloud-native insurance fraud analytics systems. Fraud is a persistent problem in the insurance industry. The already high cost of fraud and anticipatory losses is exacerbated by the increased complexity and digital transformation of corporate strategies and related information systems. Insurable transactions are often cross-border or cross-currency, with counterparties being affiliated or have close links. Detecting anomalies in cloud-based insurance platforms with many services, interdependent applications, and a global environment is especially difficult without expert help and support. The literature review examines the characteristics, types, and consequences of fraud in insurance, considering general aspects of fraud detection systems for all sectors and the characteristics of fraud detection and prevention systems for cloud-native insurance platforms. A synthesis of supervised ML techniques for fraud detection in the insurance domain is outlined, emphasizing their limitations in practical applications. The work highlights opportunities generated by generative AI to address fraud-synonymic problems more efficiently and the potential added value of generative AI in the overall context of fraud detection analytics for cloud-native insurance platforms. In conclusion, evidence from the literature points to generative AI as a significant enabler of intelligent fraud analytics in cloud-native insurance ecosystems.

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