Paper: arXiv 2308.00921

Authors: Wing Fung Chong, Daniel Linders, Zhiyu Quan, Linfeng Zhang

Abstract

In the current market practice, many cyber insurance products offer a coverage bundle for losses arising from various types of incidents, such as data breaches and ransomware attacks, and the coverage for each incident type comes with a separate limit and deductible. Although this gives prospective cyber insurance buyers more flexibility in customizing the coverage and better manages the risk exposures of sellers, it complicates the decision-making process in determining the optimal amount of risks to retain and transfer for both parties. This paper aims to build an economic foundation for these incident-specific cyber insurance products with a focus on how incident-specific indemnities should be designed for achieving Pareto optimality for both the insurance seller and buyer. Real data on cyber incidents is used to illustrate the feasibility of this approach. Several implementation improvement methods for practicality are also discussed.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a strong economic framework for unbundled, incident-specific cyber insurance using Pareto optimality proofs. It integrates real-world cyber incident data to demonstrate practical feasibility, bridging theoretical contract design with actuarial applications.

Research Flowchart

  flowchart TD
    A["Research Goal: Design Pareto-Optimal<br>Incident-Specific Cyber Insurance"] --> B["Input: Real Cyber Incident Data<br>& Loss Distribution Types"]
    B --> C["Methodology: Model Risk Retention<br>& Indemnity Coverage Bundles"]
    C --> D["Computational Optimization:<br>Solve Joint Utility Maximization"]
    D --> E["Address Implementation Improvements<br>for Practicality"]
    E --> F["Outcomes: Optimal Limits/Deductibles<br>& Pareto Efficiency for Buyer/Seller"]