Generative Adversarial Networks in Actuarial Modeling- Enhancing Predictions of Long-Tail Risks Done

Adarsh Naidu

doi:10.71097/IJSAT.v11.i3.2844

Generative Adversarial Networks in Actuarial Modeling- Enhancing Predictions of Long-Tail Risks Done

Author(s)	Adarsh Naidu
Country	United States
Abstract	Accurate assessment of long-tail risks remains one of the most complex challenges in actuarial science, particularly in modeling catastrophic events with limited historical data. This study investigates the potential of Generative Adversarial Networks (GANs) to improve traditional actuarial methodologies by generating synthetic yet realistic extreme event data. We propose LT-GAN (Long-Tail GAN), a specialized model designed to accurately capture the distributional properties of rare, high-severity insurance claims. Utilizing three diverse insurance datasets covering property, liability, and business interruption claims, we show that supplementing traditional actuarial models with GAN-generated synthetic data enhances tail risk predictions by up to 31% compared to standard techniques. Our experimental analysis demonstrates that the GAN-based framework effectively captures complex patterns in catastrophic loss events while preserving the core statistical attributes of the original datasets. This research offers an advanced framework for insurers to refine extreme risk management and improve capital adequacy calculations for rare but high-impact occurrences.
Keywords	Generative Adversarial Networks, Actuarial Science, Long-Tail Risk, Catastrophe Modeling, Extreme Value Theory, Synthetic Data Generation, Risk Management, Insurance.
Field	Engineering
Published In	Volume 11, Issue 3, July-September 2020
Published On	2020-09-03
Cite This	Generative Adversarial Networks in Actuarial Modeling- Enhancing Predictions of Long-Tail Risks Done - Adarsh Naidu - IJSAT Volume 11, Issue 3, July-September 2020. DOI 10.71097/IJSAT.v11.i3.2844
DOI	https://doi.org/10.71097/IJSAT.v11.i3.2844
Short DOI	https://doi.org/g899fb

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Generative Adversarial Networks in Actuarial Modeling- Enhancing Predictions of Long-Tail Risks Done

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