Cyber Risk Management in the Financial Services Industry, Spillover Effects and Cyber Insurance Products for Private Customers
Bibliographic record
Abstract
This thesis consists of four essays on open research questions with respect to cyber risks. The first and second research essays address cyber risk management and spillover effects from cyberattacks in the US financial services industry, while the third has a regional focus on Europe. The last essay discusses the cyber insurance products for private clients provided in six different countries. First, we empirically analyze the consciousness, determinants, and value-relevance of cyber risk management in the US banking and insurance industry, as well as the spillover effects, which to the best of our knowledge have not been examined so far. By means of a text mining approach applied to annual reports and regression analyses, we find an increasing cyber risk consciousness and point out, inter alia, that cyber risk management contributes to value creation. We also observe significant competitive effects to large- and mid-cap firms and contagion effects to the entire sample when using event study methodology. Second, as the implications of cyberattacks on announcing firms could spill over to a third party, we empirically examine spillover effects to the stocks of US (cyber) insurers in the US financial services industry which have not been investigated in closer detail. By applying an event study, we find evidence for significant contagion effects to the US (cyber) insurance industry, reflecting the deterioration of the reputation of non-announcing firms. For “mega cyberattacks”, we observe competitive effects to US cyber insurers. Studying possible influencing factors confirms that spillover effects are information-based. Third, we also empirically study the cyber risk consciousness, firm characteristics, and value of cyber risk management for European banks and insurers, as well as possible spillover effects from cyberattacks and IT risk events, which has not been done so far, as the current literature neglects the focus on Europe. When applying similar research methodologies which are in line with the two previous US studies, we correspondingly observe an increasing cyber risk consciousness and relevant determinants, as well as a positive relationship between cyber risk management and firm value, in addition to significant spillover effects. Fourth, a comparison of the cyber insurance products for private clients offered in Austria, Canada, Germany, Switzerland, the United Kingdom and the US is provided. Thereby, coverage components regarding first and third party risk, legal advice and the additional services of stand-alone cyber insurance and add-ons are identified and compared by conducting a qualitative analysis to reveal new insights regarding product features. We find that first party risk and additional services play a crucial role in stand-alone cyber insurance and add-ons, while the overall comparison of coverage components (across different regions) is challenging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".