The Use of Generative Adversarial Networks in Cyber Risk Assessment for Insurers

Authors: Adarsh Naidu

DOI: https://doi.org/10.5281/zenodo.15054645

Short DOI: https://doi.org/g8838j

Country: USA

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Abstract: In an era dominated by digital advancements, the insurance sector faces significant challenges in evaluating and pricing cyber risk policies due to the scarcity of reliable threat data and its evolving nature. This study explores the potential of Generative Adversarial Networks (GANs) to simulate cyberattacks, enhancing insurers’ capabilities to model cyber risks with greater precision. GANs, consisting of a generator and a discriminator trained through adversarial learning, can generate highly realistic synthetic attack scenarios, addressing data limitations and strengthening risk assessment frameworks. This research proposes a structured methodology for training GANs using cyber incident data and integrating synthetic outputs into actuarial models. The advantages include refined premium pricing, improved portfolio stress testing, and an enhanced understanding of emerging cyber threats. A case study [Biener, C., Eling, M., &Wirfs, J. H. (2015)] demonstrates a 40% reduction in loss prediction error with GAN-generated data. However, challenges such as training stability and ethical considerations remain. This study highlights the transformative role of GANs in cyber insurance, offering a scalable approach to adapt to dynamic cyber risks. Future research should explore advanced GAN architectures and establish ethical frameworks for implementation. This work bridges artificial intelligence and insurance, laying the foundation for innovative risk management strategies.

Keywords: Generative Adversarial Networks, Cyber Risk Assessment, Insurance, Cyberattacks, Machine Learning, Synthetic Data, Risk Modeling

Paper Id: 232264

Published On: 2023-04-03

Published In: Volume 11, Issue 2, March-April 2023

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