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Cyber Insurance Ratemaking: A Graph Mining Approach

Author

Listed:
  • Yeftanus Antonio

    (Statistics Research Division, Institut Teknologi Bandung, Bandung 40132, West Java, Indonesia)

  • Sapto Wahyu Indratno

    (Statistics Research Division, Institut Teknologi Bandung, Bandung 40132, West Java, Indonesia
    University Center of Excellence on Artificial Intelligence for Vision, Natural Language Processing & Big Data Analytics (U-CoE AI-VLB), Institut Teknologi Bandung, Bandung 40132, West Java, Indonesia)

  • Rinovia Simanjuntak

    (Combinatorial Mathematics Research Division, Institut Teknologi Bandung, Bandung 40132, West Java, Indonesia)

Abstract

Cyber insurance ratemaking (CIRM) is a procedure used to set rates (or prices) for cyber insurance products provided by insurance companies. Rate estimation is a critical issue for cyber insurance products. This problem arises because of the unavailability of actuarial data and the uncertainty of normative standards of cyber risk. Most cyber risk analyses do not consider the connection between Information Communication and Technology (ICT) sources. Recently, a cyber risk model was developed that considered the network structure. However, the analysis of this model remains limited to an unweighted network. To address this issue, we propose using a graph mining approach (GMA) to CIRM, which can be applied to obtain fair and competitive prices based on weighted network characteristics. This study differs from previous studies in that it adds the GMA to CIRM and uses communication models to explain the frequency of communications as weights in the network. We used the heterogeneous generalized susceptible-infectious-susceptible model to accommodate different infection rates. Our approach adds up to the existing method because it considers the communication frequency and GMA in CIRM. This approach results in heterogeneous premiums. Additionally, GMA can choose more active communications to reflect high communications contribution in the premiums or rates. This contribution is not found when the infection rates are the same. Based on our experimental results, it is apparent that this method can produce more reasonable and competitive prices than other methods. The prices obtained with GMA and communication factors are lower than those obtained without GMA and communication factors.

Suggested Citation

  • Yeftanus Antonio & Sapto Wahyu Indratno & Rinovia Simanjuntak, 2021. "Cyber Insurance Ratemaking: A Graph Mining Approach," Risks, MDPI, vol. 9(12), pages 1-34, December.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:12:p:224-:d:695941
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    References listed on IDEAS

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    1. Fahrenwaldt, Matthias A. & Weber, Stefan & Weske, Kerstin, 2018. "Pricing Of Cyber Insurance Contracts In A Network Model," ASTIN Bulletin, Cambridge University Press, vol. 48(3), pages 1175-1218, September.
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    3. Tse,Yiu-Kuen, 2009. "Nonlife Actuarial Models," Cambridge Books, Cambridge University Press, number 9780521764650, December.
    4. Mark Camillo, 2017. "Cyber risk and the changing role of insurance," Journal of Cyber Policy, Taylor & Francis Journals, vol. 2(1), pages 53-63, January.
    5. Maochao Xu & Lei Hua, 2019. "Cybersecurity Insurance: Modeling and Pricing," North American Actuarial Journal, Taylor & Francis Journals, vol. 23(2), pages 220-249, April.
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