International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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GAN-Based Simulation of Catastrophic Events in Insurance - Assessing Underwriting Models

Author(s) Adarsh Naidu
Country United States
Abstract The insurance industry faces significant challenges in underwriting catastrophic events such as hurricanes, earthquakes, and pandemics due to their rarity and unpredictability. Traditional underwriting models rely on historical data, which often fails to account for tail-end risks, resulting in mispriced policies and financial instability. This study explores the use of Generative Adversarial Networks (GANs) as a means to simulate synthetic catastrophic scenarios, enhancing underwriting model evaluation and refinement. By training GANs on historical disaster records and environmental variables, realistic event sequences, including spatial-temporal dynamics and economic impacts, are generated. These simulations are then integrated into standard underwriting frameworks to assess their robustness under extreme conditions. Findings reveal a 15-20% improvement in loss estimation accuracy, particularly for low-frequency, high-severity events, compared to baseline methodologies (Arjovsky, Chintala, &Bottou, 2017; Goodfellow et al., 2014). This approach provides practical advantages, such as improved risk pricing and capital allocation, while highlighting weaknesses in current underwriting practices. This research pioneers the application of advanced machine learning to insurance risk management, offering a scalable solution for emerging threats, including climate-driven disasters. Future studies could explore multi-hazard interactions and real-time applications, positioning GANs as a transformative tool in risk assessment.
Field Engineering
Published In Volume 4, Issue 5, September-October 2022
Published On 2022-10-04
DOI https://doi.org/10.36948/ijfmr.2022.v04i05.40680
Short DOI https://doi.org/g9btgx
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E-ISSN 2582-2160
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