Scaling Models for the Severity and Frequency of External Operational Loss Data
Bibliographic record
Abstract
According to Basel II criteria, the use of external data is absolutely indispensable to the implementation of an advanced method for calculating operational capital. This article investigates how the severity and frequencies of external losses are scaled for integration with internal data. We set up an initial model designed to explain the loss severity. This model takes into account firm size, location, and business lines as well as risk types. It also shows how to calculate the internal loss equivalent to an external loss, which might occur in a given bank. OLS estimation results show that the above variables have significant power in explaining the loss amount. They are used to develop a normalization formula.\n\nA second model based on external data is developed to scale the frequency of losses over a given period. Two regression models are analyzed: the truncated Poisson model and the truncated negative binomial model. Variables estimating the size and geographical distribution of the banks’ activities have been introduced as explanatory variables. The results show that the negative binomial distribution outperforms the Poisson distribution. The scaling is done by calculating the parameters of the selected distribution based on the estimated coefficients and the variables related to a given bank. Frequency of losses of more than $1 million are generated on a specific horizon.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".