Cyberspace is a dynamic ecosystem consisting of interconnected data, devices, and individuals, with multiple network layers comprising identifiable nodes. Location-based information can significantly improve cyber resilience decision-making and facilitate the development of innovative cyber risk pricing tools. This article is based on a methodology that uses company geospatial data to accurately estimate the number of expected losses arising from cyberattacks. Our approach aims to build and compare statistical spatial models that allow pricing cyber policies more effectively than traditional non-spatial methods by incorporating all available data. By accounting for spatial dependence, we can assess the risk of data breaches and contribute to the design of more efficient cyber risk policies for the insurance market.

Pricing Cyber Insurance: A Geospatial Statistical Approach / Ballestra, L. V.; D'Amato, V.; Fersini, P.; Forte, S.; Greco, F.. - In: APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY. - ISSN 1524-1904. - 40:5(2024), pp. 1365-1376. [10.1002/asmb.2891]

Pricing Cyber Insurance: A Geospatial Statistical Approach

Ballestra, L. V.;D'Amato, V.;Fersini, P.;
2024

Abstract

Cyberspace is a dynamic ecosystem consisting of interconnected data, devices, and individuals, with multiple network layers comprising identifiable nodes. Location-based information can significantly improve cyber resilience decision-making and facilitate the development of innovative cyber risk pricing tools. This article is based on a methodology that uses company geospatial data to accurately estimate the number of expected losses arising from cyberattacks. Our approach aims to build and compare statistical spatial models that allow pricing cyber policies more effectively than traditional non-spatial methods by incorporating all available data. By accounting for spatial dependence, we can assess the risk of data breaches and contribute to the design of more efficient cyber risk policies for the insurance market.
2024
Bayesian hierarchical models; cyber insurance; cyber risk; Gaussian Markov random fields; spatial correlation;
01 Pubblicazione su rivista::01a Articolo in rivista
Pricing Cyber Insurance: A Geospatial Statistical Approach / Ballestra, L. V.; D'Amato, V.; Fersini, P.; Forte, S.; Greco, F.. - In: APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY. - ISSN 1524-1904. - 40:5(2024), pp. 1365-1376. [10.1002/asmb.2891]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1727937
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