Idiosyncratic Viral Loss Theory: Systemic Operational Losses in Banks
Notice bibliographique
Résumé
Basel III regulation intent is to increase the resiliency of banks through effective risk management practices that can reduce significant idiosyncratic operational losses. A systemic risk event that leads to significant losses in a bank holding company (BHC) can expose them to become insolvent and cause significant volatility and unpredictable negative impact on the United States economy. The viral spread of operational losses through global markets by interconnected multinational banks can be compared to viruses spread through interconnected countries and the significant losses incurred; this can be referred to as idiosyncratic viral loss theory. This idiosyncratic viral loss theory discusses systemic operational losses that are evident in human error, fraud, and legal expenses that are aligned to systemic operational risk. The occurrences of significant losses that are idiosyncratic in nature and that are linked to failed internal processes, people, systems, and external events are defined by the Basel Committee on Banking Supervision as operational risk losses; these losses’ idiosyncratic nature makes them comparable to viruses. This study employs the Compliance and Ethics Group’s (OCEG’s) standard that integrates governance, risk management, internal control, assurance, and compliance (GRC capability model) into one functional goal to improve quality and principled performance through measurable tools that may enhance effectiveness and efficiency practices. This study concerns senior manager activities that can be effective towards meeting effective risk management practices posed by the Basel III regulation for BHCs, which may reduce the spread of significant losses in the banks. Through the use of a qualitative e-Delphi study, 10 banking finance experts were convened to build consensus on effective risk management practices. Data were collected from three electronic questionnaires submitted through Qualtrics. Data were analyzed using theoretical triangulation, coding, and thematic analysis. Four important considerations were identified that could bolster effective risk management practices: (a) a comprehensive enterprise-wide risk; (b) controlling fraud; (c) going beyond the minimum risk assessment requirements set forth by the banking regulators; (d) independent risk identification and management. These considerations towards effective risk management practices may help reduce systemic operational losses viral spread in banks.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».