Cyber Risk Management in the Financial Services Industry, Spillover Effects and Cyber Insurance Products for Private Customers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».