![]() A binary option on whether an event occurs is assumed to be a linear function of the expected probability of the event occurring. Prediction markets provide a way forward. This ties into Hayek's foundational work on how price systems communicate information about “how to secure the best use of resources,” even when the “knowledge of the relevant facts is dispersed among many people.” Despite outlining the intuition that prices reflect dispersed information, neither author proposes a method of extracting quantitative estimates. Abramowicz imagines insurance prices providing a rank ordering of how much litigation risk is associated with each policyholder. A novel data source for cyber loss distributions could be valuable to complement existing information sources, especially given their limitations.Ībramowicz argues the underwriting process by which insurers price and differentiate risks “can be translated into predictions”. But, throwing out such data reduces an already limited sample size. This raises the question of how relevant the losses suffered by a $300 billion energy firm are to a “mom-and-pop” retail outlet. Using the aforementioned data sources assumes homogeneity in the sample. Estimating the frequency of incidents for an individual firm is complicated by not knowing the size of the population from which these reports are drawn. This allows researchers to glean insights into the size of breaches and frequency in the aggregate. Repositories of data breach reports, such as Privacy Rights Clearinghouse, 1 are drawn from the population of firms operating in the corresponding jurisdiction. Court dockets are detailed but can only provide insights into costs assigned by courts. Self-reported survey results regarding cyber-crime costs are “dominated by a minority in the upper tail” and the effects of “refusal rates and small sample sizes” are magnified by the rarity of reported cyber attacks. The incentives for security vendors to misrepresent the threats faced have been widely recognised. Potential insights are limited by challenges including the problem of denominators, reporting biases, and the tension between sample size and granularity. How can organisations gain insights into their distribution of cyber losses? Information sources include mandatory breach notifications, threat reports released by security vendors, self-reported survey data, and court dockets relating to cyber incidents. Put simply, “risks cannot be managed better until they can be measured better”. The optimal investment level is intimately linked to both the likelihood and the impact of potential attacks. Understanding how losses are distributed is an important step in assigning information security resources. The method and resulting estimates could help organisations better manage cyber risk, regardless of whether they purchase insurance. The results suggest that the expected cyber liability loss is $428K and that the firm faces a 2.3% chance of experiencing a cyber liability loss between $100K and $10M each year. We demonstrate its value in decision support by applying it to a theoretical retail firm with annual revenue of $50M. We then aggregate the inferred loss models across 6,828 observed prices from all 26 insurers to derive the County Fair Cyber Loss Distribution. A method using particle swarm optimisation and the expected value premium principle is introduced to iterate through candidate parameterised distributions with the goal of reducing error in predicting observed prices. We provide empirical observations on how premiums vary by coverage type, amount, and policyholder type and over time. To that end, we extract cyber insurance pricing information from the regulatory filings of 26 insurers. Given the dearth of cyber security loss data, market premiums could shed light on the true magnitude of cyber losses despite noise from factors unrelated to losses. Insurance premiums reflect expectations about the future losses of each insured. ![]()
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