Idiosyncratic Viral Loss Theory: Systemic Operational Losses in Banks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".