Bibliographic record
Abstract
In 1998, an international team of bank supervisors reported that awareness of operational risk as a separate risk category was relatively low at major banks. That began to change for bankers after June 1999, when the powerful Basel Committee on Bank Supervision released a reform proposal that would have added an operations-risk-weighting factor to banks' regulatory capital ratios. Then, in April 2000, bankers' awareness hit orbital levels when a draft committee report referred to such measures as fees, commissions, gross interest income, transaction values, volumes, assets under management, and value of securities in custody, as potential standards for capital charges in certain business lines. Bankers and their trade groups quickly mobilized to protect the non-interest revenue sources built up over decades in defending against encroachments from nonbank competitors. But they had a long route to travel. Organizing for management Few banks in 1998 kept analytic records of their operational losses and causes. Any data that existed was held in the business units, with oversight of operational risk performed at a high level by the banks' directors, management committees, or audit committees. At about half the surveyed banks, an internal Operating Risk (OpR, in Basel committee-speak) monitoring role was filled either by a risk manager or by a committee, such as a product review committee. In some banks, the financial controller, chief information officer, or internal auditor took on this role. Researchers took this lack of a formal infrastructure as an indication of the immaturity of the discipline. But then, it began changing rapidly. Today, the locus of responsibility for OpR measurement and monitoring has become far more identifiable at large banks. According to the Risk Management Association, several of its members have recently started chief risk officer positions, including Bank One, Citibank, First Union, Fleet Boston, and Royal Bank of Canada. This is often a senior level position, with reporting lines up from the chief credit officer and the insurance divisions. Among their challenges, says Nick Hayes, RMA director of member relations for global financial institutions, are the creation of institution-wide operating loss databases and the development of mitigants for operational risk. To help their members organize properly and prepare for the allocation of operating risk-capital charges, members of the RMA, British Bankers Association and International Swaps and Derivatives Association had commissioned a 1999 survey by PricewaterhouseCoopers. Released in February 2000, the study identified two kinds of operational loss events: (a) frequent, relatively low-cost experiences, such as write-offs from reconciliation failures and breakdowns in cash operations or payment systems; and (b) infrequent catastrophic events, the kind often reported in the media, especially when the institutional victim declared bankruptcy. Most banks surveyed in 1998 said that not only was their tracking of operational risk at an early stage, but their metrics were very primitive. Only a few had formal measurement systems. However, even at that point, there was movement towards the use of qualitative risk factors and subjective assessments. Among these were internal audit ratings; generic operational data, such as volume, turnover and complexity; and data on quality of operations such as error rate or measures of business riskiness, such as revenue volatility. These are today being translated by bankers into grades, like audit assessments, to create a set of factors and variables that measure and model business unit risks. Organizing to model swap risk In October 2000, the International Swaps and Derivatives Association (ISDA) published a suggestion on methods of qualifying internal models. ISDA argued that qualitative criteria should be a mandatory, not optional, factor in any regulatory appraisal of operational risk management. …
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.020 |
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".