AI-Based Risk Assessment Models for Insurance Technology

  • Authors

    • Dr. Ethan Williams Associate Professor, Department of Computer Science, Kristu Jayanti College, Bengaluru, Karnataka, India. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2023PI7SJ67

    Published 03-05-2023

  • Insurance Technology, Risk Assessment, Artificial Intelligence, Machine Learning, Deep Learning, Predictive Analytics, Insurtech

    Issue

    Vol. 6 No. 1 (2023)

    Section

    Articles

    How to Cite

    [1]
    E. Williams, “AI-Based Risk Assessment Models for Insurance Technology”, IJAIDT, vol. 6, no. 1, pp. 01–14, Mar. 2023, doi: 10.67228/30713315/IJAIDT-2023PI7SJ67.

  • Abstract

    The insurance industry is evolving due to digitalization, big data, and InsurTech, making traditional actuarial models insufficient for handling complex and dynamic risks. This paper explores AI-based risk assessment using machine learning, deep learning, and hybrid models to analyze diverse data sources like IoT, telematics, and social media. The proposed framework includes data preprocessing, feature engineering, model training, validation, and deployment. AI models enable personalized underwriting, dynamic pricing, fraud detection, and proactive risk management. Results show that AI approaches outperform traditional methods in accuracy, scalability, and robustness. The study also addresses ethical, regulatory, and interpretability challenges, and suggests future directions such as federated learning and trustworthy AI for next-generation InsurTech systems.

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