A Breakthrough of Digital Assets Security in Crypto Insurance: Cyber Risk Prediction Models
Bibliographic record
Abstract
We use unsupervised cluster analysis to divide the world into geographically distinct groups and then use our proposed model to analyze the Privacy Right Clearinghouse (PRC) data breach chronology. We model zero losses using a covariate-dependent probability, moderate losses using a finite mixture distribution, and large losses using an extreme value distribution to capture the heavy-tailed nature of the loss data. The risks and opportunities that digital technologies, devices and media bring us are manifest. Cyber risk is never a matter purely for the IT team. An organisation's risk management function needs a thorough understanding of the constantly evolving risks, as well as the practical tools and techniques available to address them. It is challenging to model the whole range of losses using a typical loss distribution when considering cyber losses in terms of the number of records exposed as a result of cyber events since these losses frequently include a significant share of zeros, distinct characteristics of mid-range losses, and high losses. By suggesting a three-component splicing regression model that can concurrently simulate zeros, moderate, and substantial losses as well as take into account heterogeneous effects in mixture components, we attempt to solve this modeling dificulty. Parameters and coeffcients are estimated using the Expectation Maximization (EM) algorithm. Combining with our frequency model (generalized linear mixed model) for data breaches, aggregate loss distributions are investigated and applications on cyber insurance pricing and risk management are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".